Note. Boxplots display the interquartile range (IQR, center box), and the whiskers extend 1.5*IQR from the lower and upper hinge. The white point indicates the mean and the white center line indicates the median.


Data Preparation

Data Import

In a first step we import the raw data of the systematic review from the shared coding Google Sheet. We, import the databases of theories and scale validations as well as the database of the empirical papers. Beyond that we also import parts of the codebook for list creations.

# Import theory database (and theory search for PRISMA)
urlTheorySearch <- gsheet::construct_download_url('https://docs.google.com/spreadsheets/d/1j3j7q15lhNqPxp3qGnRtc2zuaE7plxWYR7tWKltkdU8/edit?usp=sharing',
                                             format = "csv", sheetid = "94494555")
dt.TheorySearch <- gsheet::gsheet2tbl(urlTheorySearch)
urlTheories <- gsheet::construct_download_url('https://docs.google.com/spreadsheets/d/1j3j7q15lhNqPxp3qGnRtc2zuaE7plxWYR7tWKltkdU8/edit?usp=sharing',
                                             format = "csv", sheetid = "1370266195")
dt.Theories <- gsheet::gsheet2tbl(urlTheories)

# Import scale database
urlScales <- gsheet::construct_download_url('https://docs.google.com/spreadsheets/d/1j3j7q15lhNqPxp3qGnRtc2zuaE7plxWYR7tWKltkdU8/edit?usp=sharing',
                                             format = "csv", sheetid = "1211291373")
dt.Scales <- gsheet::gsheet2tbl(urlScales)

# Import empirical database
urlPsycInfo <- gsheet::construct_download_url('https://docs.google.com/spreadsheets/d/1j3j7q15lhNqPxp3qGnRtc2zuaE7plxWYR7tWKltkdU8/edit?usp=sharing',
                                              format = "csv", sheetid = "2129881335")
dt.Empirical <- gsheet::gsheet2tbl(urlPsycInfo)

# Import input and domain codebooks
urlInput <- gsheet::construct_download_url('https://docs.google.com/spreadsheets/d/1j3j7q15lhNqPxp3qGnRtc2zuaE7plxWYR7tWKltkdU8/edit?usp=sharing',
                                             format = "csv", sheetid = "168002198")
dt.Input <- gsheet::gsheet2tbl(urlInput)
urlDomains <- gsheet::construct_download_url('https://docs.google.com/spreadsheets/d/1j3j7q15lhNqPxp3qGnRtc2zuaE7plxWYR7tWKltkdU8/edit?usp=sharing',
                                             format = "csv", sheetid = "1826792378")
dt.Domains <- gsheet::gsheet2tbl(urlDomains)

# Clean up workspace
rm(list = ls(pattern='^url'))

For the field comparisons we also import the most recent Scimago Journal Database.

# Collect all names of the individual field database files
fileNam = list.files(path = "data/JournalDatabase", pattern="scimagojr 2019  Subject Area")

# Extract field names
fieldNam <- gsub("scimagojr 2019  Subject Area - |.csv", "", fileNam) 

# import all files
fieldList <- list()
for (i in 1:length(fileNam)) fieldList[[fieldNam[i]]] <- read.csv2(paste0("data/JournalDatabase/",fileNam[i])) 

# There are some more Publishers in the individual field databases.
dfJournals <- plyr::ldply(fieldList, data.frame)
#length(unique(dfJournals$Title)) # check

# import combined file (that might miss some field-specific data)
PublisherInfo <- read.csv2("data/JournalDatabase/scimagojr 2019.csv") 

# make field labels coherent
PublisherInfo$fields <- ""
for (i in 1:nrow(PublisherInfo)) {
  for(j in 1:length(fileNam)) {
    PublisherInfo$fields[i] <- ifelse(PublisherInfo$Title[i] %in% fieldList[[fieldNam[j]]]$Title, 
                                       paste(PublisherInfo$fields[i],fieldNam[j], sep = "; "), 
                                       PublisherInfo$fields[i])
  }
}
PublisherInfo$fields <- gsub("^; ", "", PublisherInfo$fields)

# Fix column names
colNam <- gsub("\\.", "", names(PublisherInfo))
colNam <- gsub("^Title$", "PublicationTitleDb", colNam)
colNam <- gsub("^Type$", "PublicationTypeDb", colNam)
colNam <- gsub("^Country$", "PublisherCountry", colNam)
colNam <- gsub("^Region$", "PublisherRegion", colNam)
colNam <- gsub("^Publisher$", "PublisherName", colNam)

names(PublisherInfo) <- colNam

# clean up workspace
rm(fileNam, fieldNam, fieldList, colNam)

Data Cleaning

We then go on to clean the data sets in order to use them in later analyses. This step includes cleaning up variable names, extracting variables (e.g., publication year), and checking for inconsistencies between databases (all of these operations are still within the data wrangling or data munging phase).

# THEORIES
# Extract publication year (crude but seems to work)
dt.Theories$year <- str_extract(dt.Theories$Source, "([0-9]{4})")
# SCALES
# Frequency of all scales within the database
n_occur_ScaleDT <- data.frame(table(Scale = dt.Scales$Scale)) 

# Extract publication year (crude but seems to work)
dt.Scales$year <- str_extract(dt.Scales$CitationKey, "([0-9]{4})")
# EMPIRICAL
# non necessary on global level

Data Exclusions

Theories

# duplicate
dt.TheorySearch.unique <- dt.TheorySearch %>%
  filter(DuplicateAny != "TRUE")

# title screening
dt.TheorySearch.title <- dt.TheorySearch %>%
  filter(TitleScreening == 1)

# abstract screening
dt.TheorySearch.abstract <- dt.TheorySearch %>%
  filter(TitleScreening == 1,
         AbstractScreening == 1)

# full-text inclusion
dt.TheorySearch.included <- dt.TheorySearch %>%
  filter(TitleScreening == 1,
         AbstractScreening == 1,
         Extracted == 1)

# added from empirical literature
dt.Theories.Included <- dt.Theories %>%
  filter(is.na(MissingABCD))
dt.TheorySearch.added <- nrow(dt.Theories.Included)-nrow(dt.TheorySearch.included)

## reasons for exclusion ##
# title exclusion reason
theoryExclTitle <- data.frame(table(Exclusion = dt.TheorySearch$TitleNote)) %>%
  mutate(screening = "Title") %>%
  arrange(desc(Freq))

# abstract exclusion reason
theoryExclAbstract <- data.frame(table(Exclusion = dt.TheorySearch$AbstractNote)) %>%
  mutate(screening = "Abstract") %>%
  arrange(desc(Freq))

# full text exclusion reason
theoryExclFull <- data.frame(table(Exclusion = dt.TheorySearch$ExtractedReason)) %>%
  mutate(screening = "Full Text") %>%
  arrange(desc(Freq))

# # Table
# rbind(theoryExclTitle, theoryExclAbstract, theoryExclFull) %>%
#   reshape(., idvar = "Exclusion", timevar = "screening", direction = "wide") %>%
#   mutate_if(is.numeric, ~replace(., is.na(.), "")) %>%
#   rename_at(vars(starts_with("Freq.")),
#             funs(sub("Freq[.]", "", .))) %>%
#   rename(., "Exclusion Reason" = Exclusion) %>%
#   kbl(., 
#         #label = "",
#         caption = "Exclusion Reasons Theoretical Literature",
#         format = "html",
#         linesep = "",
#         booktabs = T,
#         align = c("l", "c", "c", "c"))  %>%
#   add_header_above(., c(" ", "Screening" = 3)) %>%
#   kable_styling(position = "left")

# dataframe with all exclusion reasons
theoryExclCombined <- rbind(theoryExclTitle, theoryExclAbstract, theoryExclFull) %>%
  reshape(., idvar = "Exclusion", timevar = "screening", direction = "wide") %>%
  mutate_if(is.numeric, ~replace(., is.na(.), "")) %>%
  rename_at(vars(starts_with("Freq.")),
            funs(sub("Freq[.]", "", .))) %>%
  rename(., "Exclusion Reason" = Exclusion)

At the more abstract level, the theory-specific literature search produced a total of 477 results from which we identified 73 theories. From our review of the empirical literature we added an additional 20 theories (total N = 93, for exclusion reasons see Table 1 and for a PRISMA diagram see Figure 1).

prismaTheory <- prismaGr(found = nrow(dt.TheorySearch),
         found_other = dt.TheorySearch.added,
         no_dupes = nrow(dt.TheorySearch.unique)+dt.TheorySearch.added, 
         screened = nrow(dt.TheorySearch.unique)+dt.TheorySearch.added, 
         screen_exclusions = nrow(dt.TheorySearch.unique)-nrow(dt.TheorySearch.abstract), 
         full_text = nrow(dt.TheorySearch.abstract)+dt.TheorySearch.added,
         full_text_exclusions = nrow(dt.TheorySearch.abstract)-nrow(dt.TheorySearch.included), 
         qualitative = nrow(dt.Theories.Included), 
         #quantitative = nrow(dt.Theories.Included), 
         title = "(A) PRISMA Diagram for the Theoretical Literature",
         extra_dupes_box = F,
         width = 800, height = 800)

Scales

# Past reviews
scalesPastN <- dt.Scales %>%
  dplyr::select(Source) %>%
  mutate(Source = strsplit(as.character(Source), "; ")) %>% 
  unnest(Source) %>%
  filter(Source != "own review") %>%
  na.omit %>%
  nrow

# Own review
scalesOwnN <- dt.Scales %>%
  dplyr::select(Source) %>%
  mutate(Source = strsplit(as.character(Source), "; ")) %>% 
  unnest(Source) %>%
  filter(Source == "own review") %>%
  na.omit%>%
  nrow

# after duplicate removal
scalesNoDupsN <- nrow(dt.Scales)

# remove non-available and excluded scales
dt.Scales.Included <- dt.Scales %>%
  filter(Coded == 1)
scalesAfterExclusionN <- nrow(dt.Scales.Included)

# # Table
# data.frame(table(Exclusion = dt.Scales$MissingNote)) %>%
#   arrange(desc(Freq)) %>%
#   kbl(., 
#         #label = "",
#         caption = "Scales Exclusion Reasons",
#         format = "html",
#         col.names = c("Exclusion Reason",
#                       "Frequency"), 
#         linesep = "",
#         booktabs = T,
#         align = c("l", "c"))  %>%
#   kable_styling(position = "left")

# dataframe with all exclusion reasons
scalesExcl <- data.frame(table(dt.Scales$MissingNote)) %>%
  rename("Exclusion Reason" = Var1, "Full Text" = Freq) %>%
  arrange(desc(`Full Text`))

Within the past literature we identified five major workks that reviewed the measurement of acculturation (Celenk & Van de Vijver, 2011; Maestas, 2000; Matsudaira, 2006; Wallace et al., 2010; Zane & Mak, 2004). After duplicate removal these five reviews collected a total of 97 scales. From our own review we added 159 additional validation studies. Of these scales we ultimately had to exclude 23, because they were either not accessible or did not fit the the topic of our review (see Table 1 and Figure 1).

prismaScales <- prismaGrScales(found = scalesPastN,
               found_other = scalesOwnN,
               no_dupes = scalesNoDupsN, 
               full_text = scalesAfterExclusionN,
               full_text_exclusions = scalesNoDupsN-scalesAfterExclusionN, 
               qualitative = scalesAfterExclusionN, 
               quantitative = scalesAfterExclusionN, 
               title = "(B) PRISMA Diagram for the Methodological Literature",
               extra_dupes_box = F,
               width = 800, height = 800)

Empirical

# duplicate
dt.Empirical.unique <- dt.Empirical %>%
  filter(SearchDuplicate != "TRUE")

# title screening
dt.Empirical.title <- dt.Empirical %>%
  filter(TitleScreening == 1)

# abstract screening
dt.Empirical.abstract <- dt.Empirical %>%
  filter(TitleScreening == 1,
         AbstractScreening == 1)

# full text screening
dt.Empirical.fulltext <- dt.Empirical %>%
  filter(TitleScreening == 1,
         AbstractScreening == 1,
         MissingABCD == 0)

# included (empirical)
dt.Empirical.included <- dt.Empirical %>%
  filter(TitleScreening == 1,
         AbstractScreening == 1,
         MissingABCD == 0,
         empirical != 0)

# reasons for exclusion
empiricalExclTitle <- data.frame(table(Exclusion = dt.Empirical$TitleNote)) %>%
  mutate(screening = "Title") %>%
  arrange(desc(Freq))

empiricalExclAbstract <- data.frame(table(Exclusion = dt.Empirical$AbstractNote)) %>%
  mutate(screening = "Abstract") %>%
  arrange(desc(Freq))

empiricalExclFull <- data.frame(table(Exclusion = dt.Empirical.abstract$NoteMissing)) %>%
  mutate(screening = "Full Text") %>%
  arrange(desc(Freq))

# # Table
# rbind(empiricalExclTitle, empiricalExclAbstract, empiricalExclFull) %>%
#   reshape(., idvar = "Exclusion", timevar = "screening", direction = "wide") %>%
#   mutate_if(is.numeric, ~replace(., is.na(.), "")) %>%
#   rename_at(vars(starts_with("Freq.")),
#             funs(sub("Freq[.]", "", .))) %>%
#   rename(., "Exclusion Reason" = Exclusion) %>%
#   kbl(., 
#         #label = "",
#         caption = "Exclusion Reasons Empirical Literature",
#         format = "html",
#         linesep = "",
#         booktabs = T,
#         align = c("l", "c", "c", "c"))  %>%
#   add_header_above(., c(" ", "Screening" = 3)) %>%
#   kable_styling(position = "left")

# dataframe with all exclusion reasons
empiricalEclCombined <- rbind(empiricalExclTitle, empiricalExclAbstract, empiricalExclFull) %>%
  reshape(., idvar = "Exclusion", timevar = "screening", direction = "wide") %>%
  mutate_if(is.numeric, ~replace(., is.na(.), "")) %>%
  rename_at(vars(starts_with("Freq.")),
            funs(sub("Freq[.]", "", .))) %>%
  rename(., "Exclusion Reason" = Exclusion)

At the most applied level, we assessed the broader empirical studies. This final database included the largest number of manuscripts and is in theory the application of the theoretical and methodological literature. The search produced a total of 1629 results to which we added 133 articles through contacts with experts in the field and from referenced works within the review. After duplicate removal, title–, abstract–, and full text screening we coded a total of 526 empirical works (for exclusion reasons see Table 1 and for a PRISMA diagram see Figure 1).

prismaEmpirical <- prismaGr(found = nrow(dt.Empirical),
         found_other = 133, # from Mendeley library
         no_dupes = nrow(dt.Empirical.unique), 
         screened = nrow(dt.Empirical.unique), 
         screen_exclusions = nrow(dt.Empirical.unique)-nrow(dt.Empirical.abstract), 
         full_text = nrow(dt.Empirical.abstract),
         full_text_exclusions = nrow(dt.Empirical.abstract)-nrow(dt.Empirical.fulltext), 
         qualitative = nrow(dt.Empirical.fulltext), 
         quantitative = nrow(dt.Empirical.included),
         title = "(C) PRISMA Diagram for the Empirical Literature",
         extra_dupes_box = F,
         width = 800, height = 800)

Table and Figure

exclMerged01 <- merge(theoryExclCombined, empiricalEclCombined, by = "Exclusion Reason", suffixes = c(".Theory",".Empirical"), all=T)
exclMerged02 <- merge(exclMerged01, scalesExcl %>% rename("Full Text.Scale" = "Full Text"), by = "Exclusion Reason", all=T)
reasonOrder <- c("not English", "not migration", "not migrant", "not acculturation", "not ABCD", "not theory", "not measured",
                 "items not accessible", "thesis not accessible", "article not accessible", "book not accessible", "chapter not accessible",
                 "poster not accessible", "should still be coded")

options(knitr.kable.NA = '')
exclMerged02 %>%
  slice(match(reasonOrder, `Exclusion Reason`)) %>%
  mutate(across(where(is.character), as.numeric)) %>%
  relocate("Full Text.Scale", .after = "Full Text.Theory") %>%
  kbl(., 
        #label = "",
        col.names = c("Reason", 
                      "Title", "Abstract", "Full Text",
                      "Full Text",
                      "Title", "Abstract", "Full Text"),
        caption = "Exclusion Reasons for all Literature Levels",
        format = "html",
        linesep = "",
        booktabs = T,
        align = c("l", rep("c", length(exclMerged02)-1)))  %>%
  add_header_above(., c(" ", "Theoretical" = 3, "Methodological", "Empirical" = 3)) %>%
  kable_styling(position = "left")
Table 1: Exclusion Reasons for all Literature Levels
Theoretical
Methodological
Empirical
Reason Title Abstract Full Text Full Text Title Abstract Full Text
not English 5 1 1 1 1
not migration 45 3 1 62 42 7
not migrant 24 11 4 1 65 41 6
not acculturation 49 17 16 1 225 116 12
not ABCD 7 1 29 42 5
not theory 20 71 25
not measured 1 32 35
items not accessible 16 36
thesis not accessible 1 1 33
article not accessible 1 4
book not accessible 4
chapter not accessible 1 2
poster not accessible 1 
# PrismaCombined <- manipulateWidget::combineWidgets(prismaTheory, prismaScales, prismaEmpirical, ncol = 1, byrow = TRUE)
# htmlwidgets::saveWidget(PrismaCombined, "PrismaCombined.html", selfcontained = TRUE)
# webshot2::webshot("PrismaCombined.html", "Figures/PrismaCombined.pdf", vwidth = 500, vheight = 1600)

manipulateWidget::combineWidgets(prismaTheory, prismaScales, prismaEmpirical, ncol = 1, byrow = TRUE)

Figure 1: Prisma diagram for three data sets on psychological acculturation.

Full Databases

Theoretical Literature

The 93 theoretical works are listed in Table 2.

dt.Theories.Included %>%
  dplyr::select(Theory, Reference = CitationKey,
                #Affect, Behavior,  Cognition, Desire,
                Affect = AffectFinal, Behavior = BehaviorFinal, Cognition = CognitionFinal, Desire = DesireFinal,
                `Type of Theoretical Work` = FrameworkTheoryModel, Focus = GeneralAspect, `Migration Time` = Time, `Source Type` = SourceType) %>%
  mutate_at(vars(Affect, Behavior, Cognition, Desire), ~replace_na(., 0)) %>%
  mutate(Reference = paste0("@",Reference)) %>%
  kbl(., caption = "Empirical Literature",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
   scroll_box(width = "110%", height = "750px")
Table 2: Empirical Literature
Theory Reference Affect Behavior Cognition Desire Type of Theoretical Work Focus Migration Time Source Type
Acculturation and Adaptation Framework Berry (1992) 1 1 1 1 Framework General dynamic theoretical
Acculturation as Developmental Pathways David Lackland Sam & Oppedal (2003) 0 1 1 0 Theory General dynamic theoretical
Acculturation as Histero-Socio-Political Dependence Bhatia & Ram (2009) 1 1 1 0 Framework General dynamic empirical
Acculturation Exclusion Framework Leong (2014a) 0 1 1 0 Framework General static theoretical
Acculturation Intentions Model Tartakovsky (2012) 1 1 1 1 Model General dynamic theoretical
Acculturation Strategies Berry (1998) 0 1 1 0 Framework General static theoretical
Acculturation Strategies - revised Berry (2005) 0 1 1 0 Framework General static theoretical
Acculturation-Salutogenesis Framework Riedel et al. (2011) 1 1 1 0 Framework Aspect dynamic theoretical
Acculturative Family Distancing (AFD) Hwang (2006) 1 1 1 0 Theory Aspect dynamic theoretical
Ambiguous Loss of Homeland Framework Rose M. Perez & Arnold-Berkovits (2018) 1 0 1 0 Framework Aspect static empirical
Assimilation Theory Gordon (1964) 0 1 1 0 Theory General dynamic theoretical
Bicultural Competence LaFromboise et al. (1993) 1 1 1 1 Conceptualization General dynamic theoretical
Bicultural identity integration Benet-Martínez & Haritatos (2005) 1 0 1 0 Framework Aspect static theoretical
Bicultural Self-Efficacy Framework Ballard-Kang (2017) 1 1 1 0 Framework Aspect static theoretical
Borderlands Theory Anzaldúa (1987) 1 1 1 1 Framework General dynamic theoretical
Capital-Field-Habitus Framework to Migrant Careers Joy et al. (2018) 0 1 1 0 Framework Aspect dynamic theoretical
Comparative Integration Context Theory Crul & Schneider (2010) 1 1 1 0 Theory General static empirical
Concordance Model of Acculturation Piontkowski et al. (2002) 0 1 1 0 Model General static theoretical
Cultural Awareness and Ethnic Loyalty Model Padilla (1980) 0 1 1 0 Model General static theoretical
Cultural Congruence Framework Robinson (2019) 0 1 1 1 Framework General dynamic theoretical
Cultural Fusion Theory Croucher & Kramer (2017) 0 1 1 1 Theory General dynamic theoretical
Cultural Learning Theory Masgoret & Ward (2006) 0 1 1 0 Theory General static theoretical
Cultural maintenance-contact-adoption model Ward & Kus (2012) 0 1 1 0 Model General static theoretical
Cultural Transnationalism Tsuda (2012) 0 1 0 0 Conceptualization Aspect static theoretical
Dialogical Self Theory (at Times of Migration) Abreu et al. (2013) 1 1 1 1 Theory Aspect dynamic theoretical
Ecological Acculturation Framework (EAF) Salo & Birman (2015) 0 1 0 1 Framework General static theoretical
Ecological Acculturation Process Theory Ward & Geeraert (2016) 1 1 1 0 Theory General dynamic theoretical
Ecological Model of Acculturation and Immigrant Mental Health Serdarevic & Chronister (2005) 1 1 1 0 Model General dynamic theoretical
Enculturation - Identity Structure Analysis Weinreich (2009) 0 0 1 1 Conceptualization Aspect static theoretical
Episodic Framework of Outgroup Interaction Processing Maertz et al. (2016) 1 1 1 1 Framework General dynamic theoretical
Ethnic Identity Development Theory Phinney (1990) 1 1 1 0 Theory General dynamic theoretical
Existential Migration Concept Madison (2006) 1 1 1 1 Conceptualization Aspect dynamic empirical
Family Beliefs Framework Weine et al. (2006) 1 1 1 0 Framework Aspect static empirical
Framework for Migrant Education Performance Alitolppa-Niitamo (2004) 0 1 1 0 Framework Aspect static theoretical
Framework of Acculturation Variables Arends-Tóth & Vijver (2006) 1 1 1 0 Framework General static empirical
Grounded theory of work adaptation for cross-regime migrants B. J. Kim & Rousseau (2019) 0 1 1 1 Theory Aspect static empirical
Immigrant Consumer Acculturation Peñaloza (1989) 0 1 1 1 Model Aspect static theoretical
Integrative Theory of Communication and Cross-Cultural Adaptation Y. Y. Kim (1988) 1 1 1 1 Framework General dynamic theoretical
Interactive Acculturation Model (IAM) Bourhis et al. (1997) 0 1 1 0 Model General dynamic theoretical
Intercultural Adjustment Conceptualization Grove & Torbiörn (1985) 1 1 1 1 Model General dynamic theoretical
Intercultural Network Theory Smith (1999) 0 1 1 0 Theory General static theoretical
Liminal Integration Lens Loon & Vitale (2021) 0 1 1 0 Model Aspect dynamic theoretical
Liminality in Cultural Transition Martha B. Baird & Reed (2015) 1 1 1 0 Framework Aspect static empirical
Migrant Experience Framework Rogler (1994) 0 1 1 0 Framework General static theoretical
Migratory Careers Conceptualization Martiniello & Rea (2014) 0 1 1 1 Theory General dynamic theoretical
Model of Acculturative Process Through Choice Wood (2014) 0 1 1 1 Framework General static empirical
Model of recursive consumer cultural adaptation Luedicke (2011) 0 1 1 1 Model Aspect static theoretical
Multi-Directional Model of Migrants’ Attitudes (Tri-Dimensional Model) Cohen (2011) 0 0 1 0 Model General dynamic theoretical
Multidimensional Acculturation Theory Schwartz et al. (2010) 0 1 1 0 Theory General dynamic theoretical
Multidimensional Individual Difference Acculturation Safdar et al. (2003) 0 1 1 1 Model General dynamic theoretical
Multifocal Integration Kemppainen et al. (2020) 1 1 1 0 Model General static theoretical
Multilevel framework of international skilled migrants Hajro et al. (2019) 1 1 1 1 Framework General static theoretical
Multiple Psychological Sense of Community Mannarini et al. (2017) 1 1 1 1 Theory General static theoretical
Navigating Across Cultures Model Mistry & Wu (2010) 1 1 1 0 Model General dynamic theoretical
Negotiating Boundaries Theory S. S. Kim (2004) 1 1 1 1 Theory General dynamic empirical
Negotiating Integration through Occupation Theory Huot et al. (2013) 0 1 1 1 Theory General dynamic empirical
Network Commitment Ethnic Identity Model Epstein & Heizler (2015) 0 1 0 0 Model Aspect static theoretical
New Assimilation Theory Alba & Nee (1997) 0 1 1 0 Theory General static theoretical
Nigrescence Theory (+ Revision) Cross (1991) 1 1 1 1 Theory Aspect dynamic theoretical
Overall Immigrant Incorporation Model Enriquez (2014) 1 1 1 1 Model General dynamic empirical
Paradigm model of the acculturation process of immigrant adolescents Sookjung Lee (2018) 1 1 1 1 Theory General static empirical
Political Transnationalism Tsuda (2012) 1 1 1 0 Conceptualization Aspect static theoretical
Professional Adaptation Processes Theory Rogler (1994) 0 1 1 1 Theory Aspect dynamic theoretical
Psychological and Sociocultural adaptation Searle & Ward (1990) 1 1 0 0 Theory General static theoretical
Psychosocial Model of Acculturation (Behavioral and Value Acculturation) Szapocznik et al. (1978) 0 1 1 0 Theory General dynamic theoretical
Race relations cycle R. E. Park & Burgess (1921) 0 1 1 0 Theory General dynamic theoretical
Minority Identity Development Model Atkinson et al. (1993) 1 1 1 1 Framework Aspect dynamic theoretical
Relative Acculturation Extended Model Navas et al. (2005) 0 1 1 1 Model General static theoretical
Remote Acculturation Ferguson & Bornstein (2015) 0 1 0 0 Conceptualization Aspect static theoretical
Resilience Acculturation Framework Pan (2014) 1 1 0 0 Framework General dynamic theoretical
Resource-Based Model of Migrant Adaptation Ryan et al. (2008) 1 1 1 1 Model General dynamic theoretical
Segmented assimilation theory Portes & Zhou (1993) 0 1 1 0 Theory General dynamic theoretical
Segmented Assimilation Theory - Revised Portes et al. (2009) 0 1 1 0 Theory General dynamic theoretical
Self-Silencing in Cultural Adaptation Experience Theory A. Ali (2010) 1 1 1 1 Theory General dynamic empirical
Social Cognitions, Social Identity, and Social Stigma Framework of Acculturation Padilla & Perez (2003) 1 1 1 1 Framework General dynamic theoretical
Social Media Acculturation Theory Croucher (2011) 0 1 1 1 Model Aspect static theoretical
Socio-Economic Transnationalism Tsuda (2012) 0 1 0 0 Conceptualization Aspect static theoretical
The Extended Bidimensional Model of Acculturation Ben-Shalom & Horenczyk (2003) 0 1 1 0 Model General static empirical
Theory of Cultural Adaptability Ramakrishnan et al. (2018) 0 1 1 1 Theory General dynamic theoretical
Theory of Cultural Transmission in Minorities Mchitarjan & Reisenzein (2013) 0 1 1 1 Theory General dynamic theoretical
Theory of Imported Political Socialization Wals Aparicio (2009) 0 1 1 0 Theory Aspect dynamic theoretical
Theory of Intergenerational Integration Esser (2004) 0 1 1 1 Theory General dynamic theoretical
Theory of Living with the Choice Davenport (2017) 1 1 1 1 Theory General dynamic empirical
Theory of Marginality Del Pilar & Udasco (2004) 1 1 1 0 Theory General static theoretical
Theory of Well-Being in Refugee Women Experiencing Cultural Transition Martha B. Baird (2012) 1 1 1 0 Theory General dynamic empirical
Transitions Theory Meleis et al. (2000) 1 1 1 1 Theory General dynamic theoretical
Transnational Identities Tsuda (2012) 0 1 1 0 Conceptualization Aspect static theoretical
Transnational Life Courses Theory Karim (2021) 1 1 0 0 Model General dynamic theoretical
Transnational Migration Theory Basch et al. (1994) 0 1 0 0 Conceptualization General static theoretical
Transnational Social Spaces Faist (2000) 0 1 1 0 Theory General static theoretical
Tridimensional Acculturation Ferguson et al. (2012) 1 1 1 1 Model General static theoretical
Vitality Theory (Subjective) Giles et al. (1977) 1 0 1 0 Theory Aspect static theoretical
Well-Being of Immigrant Youth Framework Nolan (2012) 0 1 1 0 Framework Aspect static theoretical

Methodological Literature

The 233 scales are listed in Table 3.

dt.Scales.Included %>%
  mutate(SourceShort = stri_replace_all_fixed(Source,
                                         pattern = c("@Celenk2011", "@Maestas2000", "@Matsudaira2006", "@Wallace2010", "@Zane2004", "own review"), 
                                         replacement = c("CEL", "MAE", "MAT", "WAL", "ZAN", "OWN"), 
                                         vectorize_all = FALSE)) %>%
  dplyr::select(Scale, Reference = CitationKey,
                #Affect,    Behavior,   Cognition,  Desire,
                Affect = AffectFinal, Behavior = BehaviorFinal, Cognition = CognitionFinal, Desire = DesireFinal,
                `Source ^a^` = SourceShort, Sample, `Majority Included` = IncludesMajority, 
                `Country of Settlement` = HostCountry,  `Country of Origin` = OriginCountry) %>%
  mutate_at(vars(Affect,    Behavior,   Cognition, Desire), ~replace_na(., 0)) %>%
  mutate(Reference = paste0("@",Reference)) %>%
  kbl(., caption = "Acculturation Scales",
      format = "html") %>%
  add_footnote(c("CEL = @Celenk2011, MAE = @Maestas2000, MAT = @Matsudaira2006, WAL = @Wallace2010, ZAN = @Zane2004, OWN = own review (only additional)"), 
               notation = "alphabet") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
   scroll_box(width = "110%", height = "750px")
Table 3: Acculturation Scales
Scale Reference Affect Behavior Cognition Desire Source a Sample Majority Included Country of Settlement Country of Origin
Abbreviated Multidimensional Acculturation Scale (AMAS) Zea et al. (2003) 1 0 1 0 CEL; MAT; WAL general 0 United States of America LatinX
Abridged General Ethnicity Questionnaire—American version (GEQA) Tseng et al. (2015) 0 1 1 0 OWN women 0 United States of America China
Acceptance of American Cultural Values Moon & Park (2007) 1 1 1 0 OWN general 0 United States of America South Korea
Acculturation and Nutrition Needs Assessment N. Fitzgerald et al. (2006) 0 1 0 0 WAL
Acculturation Attitude Scale (AAS-16) Schmitz & Schmitz (2012) 0 0 1 1 OWN general 0 Germany Turkey, North Africa
Acculturation Attitude Scale (Safdar, Struthers, & van Oudenhoven, 2009) Safdar et al. (2009) 0 0 1 0 OWN general 0 United States of America, Netherlands, United Kingdom Iran
Acculturation Attitude Scale (Sam, 1995) David Lackland Sam (1995) 0 0 1 1 OWN youth 0 Norway Third World
acculturation attitudes - four statements (Arends-Tóth & van de Vijver, 2007) Arends-Tóth & Vijver (2007) 0 1 1 0 OWN general 0 Netherlands Turkey
acculturation attitudes - one statement (Arends-Tóth & van de Vijver, 2007) Arends-Tóth & Vijver (2007) 0 1 1 0 OWN general 0 Netherlands Turkey
acculturation attitudes - two statements (Arends-Tóth & van de Vijver, 2007) Arends-Tóth & Vijver (2007) 0 1 1 0 OWN general 0 Netherlands Turkey
Acculturation Attitudes (ICSEY) ICSEY team (2006) 0 0 1 1 OWN youth 0 multiple multiple
Acculturation Attitudes (Mancini, & Bottura, 2014) Mancini & Bottura (2014) 0 0 0 1 OWN youth 1 Spain any
Acculturation Attitudes Scale (Kim, 1988) U. Kim (2010) 1 1 1 1 OWN general 0 Canada South Korea
Acculturation Attitudes Scale (Sam & Berry, 1995) David L. Sam & Berry (1995) 0 0 1 1 CEL; MAT youth 0 Norway Third World
Acculturation behaviors - peer contacts & activities (ICSEY) ICSEY team (2006) 0 1 0 0 OWN youth 0 multiple multiple
Acculturation in Australia Liu (2007) 1 0 1 0 OWN general 1 Australia Asia
Acculturation Index (AI; Ward & Kennedy, 1994) Ward & Kennedy (1994) 0 1 1 0 MAT general 0 multiple New Zealand
Acculturation Index (NESARC) Kimber et al. (2015) 0 1 1 1 OWN general 1 United States of America any
Acculturation Index (Ward, & Rana-Deuba, 1999) Ward & Rana-Deuba (1999) 0 1 1 0 CEL general 0 Nepal any
Acculturation Indicators (Needham, et al., 2017) Needham et al. (2017) 0 1 0 1 OWN general 0 United States of America South Asia
Acculturation Intergenerational Conflict Scale (AIC) J.-S. Lee (2004) 1 1 1 1 OWN youth 0 United States of America South Korea
Acculturation Modes for Koreans (Lee, Sobal, and Frongillo, 2003) Soo-kyung Lee et al. (2003) 1 1 1 0 OWN general 0 United States of America South Korea
Acculturation Orientation (based on Horenczyk, 1996, 2000) Ben-Shalom & Horenczyk (2003) 1 0 1 1 OWN soldiers 0 Israel former Soviet Union
Acculturation Orientation (Demes, & Geeraert, 2014) Demes & Geeraert (2014) 0 1 0 0 OWN general, student 0 United Kingdom any
Acculturation preferences (Christ et al., 2013) Christ et al. (2013) 0 0 1 0 OWN general 1 Germany any
Acculturation Questionnaire for Children Van De Vijver et al. (1999) 0 0 1 0 MAT youth 0 Netherlands any
Acculturation Rating Scale for Arab-American-II Jadalla & Lee (2015) 1 1 1 0 OWN general 0 United States of America Arabic Countries
Acculturation Rating Scale for Mexican Americans Cuellar et al. (1980) 0 1 1 0 CEL; MAE; MAT; WAL; ZAN clinical 0 United States of America Mexico
Acculturation Rating Scale for Mexican-Americans - Revised (ARSMA-II) Cuellar et al. (1995) 1 1 1 0 CEL; MAE; MAT; WAL; ZAN students 1 United States of America Mexico
Acculturation Rating Scale for Mexican-Americans- Short Form Dawson et al. (1996) 0 1 1 0 CEL; MAE; WAL general 0 United States of America Hispanic
Acculturation Scale (ACC) (Abbas et al.) Abbas et al. (2018) 0 1 1 0 Abbas et al. (2018) general 0 Australia Muslims
Acculturation Scale (Arends-Tóth & van de Vijver, 2000) Arends-Tóth & Vijver (2000) 0 1 1 0 OWN general 1 Netherlands any
Acculturation Scale (Cheung, 1995) Cheung (1995) 1 1 1 0 OWN general 0 New Zealand Cambodia
Acculturation Scale (Ghuman, 1991) P. A. S. Ghuman (1991) 1 0 1 1 MAT youth 0 United Kingdom any
Acculturation Scale (Ghuman, 1997) Paul A. Singh Ghuman (1997) 0 0 1 1 CEL youth 0 United Kingdom India
Acculturation Scale (Ghuman, 2000) Paul A. Singh Ghuman (2000) 0 0 1 1 MAT youth 0 Australia South Asia
acculturation scale (Wang, 2007) C. Wang (2007) 0 1 1 0 OWN general 1 United States of America China
Acculturation Scale for Asian Indians (ASAI) Parekh (2000) 1 1 1 1 OWN general 0 United States of America India
Acculturation Scale for Southeast Asians Anderson et al. (1993) 0 1 1 0 MAE; MAT; ZAN general 0 United States of America Southeast Asia
Acculturation Scale for Vietnamese Adolescents H. H. Nguyen et al. (1999) 1 1 1 1 CEL; MAE; MAT youth 0 United States of America Vietnam
Acculturation Scale for Vietnamese Adolescents (ASVA) H. H. Nguyen & Von Eye (2002) 1 1 1 1 OWN students 0 United States of America Vietnam
Acculturation Strategies (Mancini, & Bottura, 2014) Mancini & Bottura (2014) 0 1 0 0 OWN youth 1 Spain any
Acculturation Strategies and Attitudes Questionnaire, immigrants’ version Luque et al. (2004) 1 1 1 0 OWN general 1 Spain any
Acculturation to American Core Culture Ruesch et al. (1948) 1 1 1 1 OWN general 1 United States of America any
Acculturation, Habits and Interests Multicultural Scale for Adolescents (AHIMSA) Unger et al. (2002) 1 1 1 0 CEL; WAL youth 1 United States of America any
Acculturative Dissonance (AD) T. N. Le et al. (2009) 0 1 1 0 OWN youth 0 United States of America Cambodia, China, Laos, Vietnam
Acculturative Hassles Vinokurov et al. (2002) 0 1 0 0 CEL refugee 0 Russia, United States of America former Soviet Union
Acculturative Stress Inventory for Children Suarez-Morales et al. (2007) 1 0 1 0 CEL youth 0 United States of America Hispanic
Acculturative Stress Scale Snyder (1987) 1 1 1 0 CEL general 0 United States of America Mexico
Adaptation Scale (Gao, & Gudykunst, 1990) G. Gao & Gudykunst (1990) 1 1 1 0 OWN students 0 United States of America any
adapted Acculturation Rating Scale for Mexican-Americans-II (ARSMA-II) (Schaefer, et al., 2009) Schaefer et al. (2009) 0 1 1 0 OWN youth women 0 United States of America Mexico, Asia
Adapted Biculturalism Scale (Nagaraj, Vyas, McDonnell, DiPietro) Nagaraj et al. (2018) 1 0 1 0 OWN women 0 United States of America South Asia
adapted Ethnic/Cultural Identity Dimensions Scale (EID Scale) Liu (2006) 1 0 1 1 OWN general 0 Australia China
Adapted Measurement of Acculturation Strategies for People of African Descent (MASPAD) - UK Onyigbuo et al. (2018) 1 1 1 0 OWN general 0 United Kingdom Nigeria
adapted Mexican American Cultural Values Scale (MACVS) Duarte (2020) 0 0 1 0 OWN general 1 United States of America Mexico
Adjustments to U.S. National Culture Alkhazraji et al. (1997) 0 1 1 0 OWN Muslims 1 United States of America any
Adopt and Keep Scale Swaidan et al. (2006) 0 1 1 0 CEL general 0 United States of America Middle East, Asia
Affinity for Korean Cultural Identity Moon & Park (2007) 1 1 1 0 OWN general 0 United States of America South Korea
African American Acculturation Scale (Landrine & Klonoff, 1994) Landrine & Klonoff (1994) 1 1 1 0 CEL; MAT; ZAN general 1 United States of America United States of America
African American Acculturation Scale (Snowden & Hines, 1999) Snowden & Hines (1999) 1 1 1 0 MAT; ZAN general 0 United States of America United States of America
African American Acculturation Scale Revised (Klonoff & Landrine, 2000) Klonoff & Landrine (2000) 1 1 1 0 MAT general 0 United States of America United States of America
American and Puerto Rican Cultural Involvement Scales Cortés et al. (1994) 1 0 1 1 MAT; WAL; MAE general 0 United States of America Puerto Rico
American Puerto Rican Acculturation Scale Cortés et al. (2003) 1 0 1 1 CEL general 0 United States of America Puerto Rico
anticipated socio-cultural adaptation scale Yijälä et al. (2012) 0 0 1 0 OWN general 0 Finnland Russia
Asian American Multidimensional Acculturation Scale (AAMAS; Chung, Kim, & Abreu, 2004) Gim Chung et al. (2004) 1 1 1 1 CEL; MAT students 0 United States of America Asia, South Korea
Asian Indian Dietary Acculturation Measure (AIDAM) Venkatesh et al. (2017) 1 1 1 0 OWN general 0 United States of America Asian Indian
Asian Values Scale Bryan S. K. Kim et al. (1999) 0 0 1 0 MAT; ZAN students 1 United States of America Asia
Asian-American Values Scale—Revised (AAVS-R) Bryan S. K. Kim & Hong (2004) 0 0 1 0 OWN students 0 Asia United States of America
Assimilation Scale (Cernovsky, 1990) Cernovsky (1990b) 1 1 1 1 OWN refugees 0 Switzerland Czechoslovakia
Assimilation to French Culture Badea (2012) 0 1 1 0 OWN general 1 France France
attitudes toward homosexuality (ESS) R. Fitzgerald et al. (2014) 0 0 1 0 OWN general 1 Europe any
Attitudes towards minority culture maintenance and inter-ethnic contact scale Nigbur et al. (2008) 0 0 1 0 OWN youth 1 United Kingdom any
Banal Integration Ekmanis (2017) 1 1 1 1 OWN general 0 Latvia Russia
Behavioral Acculturation Scale Szapocznik et al. (1978) 0 1 1 1 MAE; MAT; ZAN general 1 United States of America Cuba
Benet-Martinez Acculturation Scale Benet‐Martínez (2006) 1 1 1 1 CEL ? ? United States of America LatinX, Cuba
Bicultural Identity Integration Scale (BIIS-1) Benet-Martínez & Haritatos (2005) 1 0 1 0 CEL general 0 United States of America China
Bicultural Identity Integration Scale (BIIS-2) Huynh et al. (2018) 1 0 1 0 CEL students 0 United States of America any
Bicultural Involvement Questionnaire Szapocznik et al. (1980) 1 0 0 1 CEL; MAT; ZAN youth 0 United States of America Cuba, Hispanic
Bicultural Youth Acculturation Questionnaire Kukaswadia et al. (2016) 1 0 1 0 OWN general 1 Canada any
Biculturalism/Multiculturalism Experience Inventory M. Ramirez (1983) 1 1 0 0 MAT; ZAN general 0 United States of America Hispanic
Bidimensional Acculturation Scale for Hispanics Marín & Gamba (1996) 0 1 1 0 CEL; MAE; MAT; WAL; ZAN general 0 United States of America Hispanic
Bidimensional acculturation scale for Korean Americans Jang et al. (2007) 0 1 1 0 OWN general 0 United States of America South Korea
Bidimensional Acculturation Scale for Marriage-Based Immigrants (BASMBI) H.-H. Chen & Chien (2018) 0 1 1 0 OWN husband men 0 Taiwan any
Brief Acculturation Rating Scale for Mexican Americans–II (ARSMA II- SF) Cuellar et al. (2004) 0 1 1 0 OWN elderly 0 United States of America Mexico
Brief Acculturation Scale Meredith et al. (2000) 0 1 1 0 CEL; MAE; MAT general 0 United States of America Japan
Brief Acculturation Scale for Hispanics Norris et al. (1996) 1 1 1 0 CEL; MAT; WAL; ZAN youth 0 United States of America Hispanic
Brief Sense of Community Scale (BSCS-8) Peterson et al. (2008) 1 1 1 1 OWN general 1 United States of America LatinX
Chicano Adolescent Acculturation Scale Olmedo et al. (1978) 0 1 1 0 MAE; WAL youth 1 United States of America Mexico
Children’s Acculturation Scale Franco (1983) 0 1 1 0 CEL; MAE; MAT; ZAN youth 0 United States of America Mexico
Children’s Hispanic Background Scale Martinez et al. (1984) 0 1 1 0 CEL; WAL; ZAN youth 0 United States of America Mexico
CHIS Acculturation Score Johnson-Kozlow (2010) 0 1 1 0 OWN general 0 United States of America Mexico
Civic engagment measure Uslaner & Brown (2005) 0 1 0 0 OWN general 1 United States of America any
Civic involvement, donation and observance of cultural practices scale B.-H. Kim (2013) 0 1 1 0 OWN general 0 Canada South Korea
Cultual Adaptation Korean Elderly Jang et al. (2007) 0 1 1 0 OWN elderly 0 United States of America South Korea
cultural adaptation (Jeong, & Kim, 2016) Jeong & Kim (2016) 0 0 1 0 OWN refugee women 0 South Korea North Korea
Cultural Awareness-Ethnic Loyalty Scale Keefe & Padilla (1987) 0 1 1 0 MAE; WAL general 0 United States of America Mexico
Cultural Beliefs and Behaviors Adaptation Profile (CBBAP) Bogumill (1998) 0 1 1 0 OWN general 0 United States of America China, Europe
Cultural Health Attributions Questionnaire Murguía et al. (2000) 0 0 1 0 WAL general 0 United States of America Hispanic
Cultural identity (ICSEY) ICSEY team (2006) 1 0 1 0 OWN youth 0 multiple multiple
Cultural Identity and Biculturality Mchitarjan & Reisenzein (2015) 1 1 1 0 OWN general 0 any any
Cultural Life Styles Inventory Mendoza (1989) 1 1 1 1 CEL; MAE; MAT; ZAN general 1 United States of America Mexico
Cultural Readjustment Rating Questionnaire Spradley & Phillips (1972) 1 1 1 0 CEL students 1 United States of America China
Cultural Values Conflict Scale Inman et al. (2001) 1 0 1 0 MAT women 0 United States of America South Asia
Culture Shock Questionnaire Mumford (1998) 1 1 1 1 CEL general 0 multiple United Kingdom
Culture-Transmission Motive Mchitarjan & Reisenzein (2015) 1 0 1 1 OWN general 0 any any
Demands of Immigration Scale Aroian et al. (1998) 1 1 1 1 OWN
Downward Assimilation Index Portes et al. (2009) 0 1 0 0 OWN
Dutch Psychological Acculturation Scale Stevens et al. (2004) 1 1 1 0 OWN general 0 Netherlands Marocco
East Asian Acculturation Measure D. Barry (2001) 1 1 1 0 OWN general 0 United States of America China, Japan, South Korea
Enculturation Measure for Native American Youth Zimmerman et al. (1996) 1 1 1 0 MAT youth 0 United States of America Native
Ethnic/Cultural Identity Dimensions Scale (EID Scale) Ting-Toomey et al. (2000) 1 1 1 0 OWN general 1 United States of America any
Ethnic/Cultural Identity Salience Questionnaire Kouli & Papaioannou (2009) 1 1 1 1 OWN student 1 Greece any
Ethno-cultural Identity Conflict Scale Ward et al. (2011) 1 0 1 0 OWN general 1 New Zealand any
Ethnocultural Identity Behavior Index Yamada et al. (1998) 0 1 0 0 OWN student 0 United States of America Asia, Hawaii
European American Value Scale for Asian Americans Wolfe et al. (2001) 0 0 1 0 MAT students 1 United States of America Asia
European-American Values Scale for Asian-Americans—Revised (EAVS-AA-R) Sehee Hong et al. (2005) 0 0 1 0 OWN students 0 Asia United States of America
Expatriate Acculturation Index Scale Lineberry (2012) 0 1 1 0 OWN general 0 any United States of America
Expatriate Cross-Cultural Adjustment Scale Black & Stephens (1989) 0 1 1 0 OWN general 0 United States of America Japan, South Korea, Taiwan, Hong Kong
Family relationship values (ICSEY) ICSEY team (2006) 0 0 1 0 OWN youth 0 multiple multiple
Food Similarity Index (FSI) Van Hook et al. (2015) 0 1 0 0 OWN general 1 United States of America any
Frankfurt Acculturation Scale (FRAKK) Bongard et al. (2002) 1 1 1 1 OWN student men 1 Germany Turkey
General Acculturation Index (GAI) Balcazar et al. (1995) 1 1 0 0 OWN women 0 United States of America Mexico
General Ethnicity Questionnaire J. L. Tsai et al. (2000) 1 1 1 1 CEL; ZAN students 0 United States of America China
Ghanaian Acculturation Survey (GAS) Eshun (2006) 0 1 1 0 OWN general 0 United States of America Ghana
global acculturation indexes Berry & Sabatier (2011) 0 0 1 1 OWN youth 0 Canada, France any
Greek Immigrant Acculturation Scale Madianos et al. (2008) 0 1 1 1 OWN general 0 Greece any
Greek-American Acculturation Scale Harris & Verven (1996) 1 1 1 1 MAT general 1 United States of America Greece
Hawaiian Culture Scale Adolescent version Hishinuma et al. (2000) 0 1 1 0 MAT students 1 United States of America Native
Hazuda Scale Hazuda et al. (1988) 0 1 1 0 MAT general 1 United States of America Mexico
Homesickness and Contentment Scale Heajong Shin & Abell (1999) 1 1 1 1 CEL students 0 United States of America China, South Korea
Identification Scale (Renvik et al., 2018) Renvik et al. (2018) 1 0 1 0 OWN general 0 Finland former Soviet Union
Identity Style Inventory — Cultural Transition Version Szabo et al. (2016) 0 1 1 1 OWN general New Zealand any
Immigrant Identity Commitment Scale Szabo et al. (2016) 0 0 1 1 OWN general New Zealand any
Immigrant Identity Questionnaire (IIQ) Orr et al. (2003) 1 1 1 1 OWN youth 0 United States of America Ethiopia, former Soviet Union
Immigration Policy Lab (IPL-12) Integration Index - 12 items Harder et al. (2018) 1 1 1 1 OWN general 0 United States of America, Germany any
Immigration Policy Lab (IPL-24) Integration Index - 24 items Harder et al. (2018) 1 1 1 1 OWN general 0 United States of America, Germany any
Indicators of acculturation domains (RAEM) Navas et al. (2005) 0 1 1 1 OWN N/A 1 N/A N/A
Integration Efforts Guest & Stamm (1993) 0 1 0 0 OWN general 0 United States of America any
Integration Well-being Scale (Amit, Litwin) Amit & Litwin (2010) 1 1 1 0 OWN elderly 0 Israel any
Intercultural Adjustment Potential Scale (ICAPS) Matsumoto et al. (2001) 1 1 1 0 OWN general 0 United States of America Japan
interethnic contact Martinovic et al. (2009) 0 1 0 0 OWN general 0 Netherlands any
Internal-External Ethnic Identity Measure Kwan & Sodowsky (1997) 0 1 1 1 CEL general 0 United States of America China
Iranian Acculturation Scale Shahim (2007) 0 1 1 1 OWN general 0 Canada Iran
Israeli religio-ethnic acculturation Rebhun (2014) 0 1 0 0 OWN general 0 any Israel
Italian Ethnic Identity Measure Laroche et al. (2005) 0 1 1 0 CEL general 0 Canada Italy
Italian-Canadian Acculturation Scale (Kim, Laroche, & Tomiuk, 2001) C. Kim et al. (2001) 1 1 1 1 OWN general 0 Canada Italy
Ladakh Acculturation Scale – Tridimensional (LAS-T) Scale Ozer & Schwartz (2016) 1 1 1 1 OWN general 1 India India
Language Proficiency (ICSEY) ICSEY team (2006) 0 0 1 0 OWN youth 0 multiple multiple
Language Use (ICSEY) ICSEY team (2006) 0 1 0 0 OWN youth 0 multiple multiple
Language-based Acculturation Scale Deyo et al. (1985) 0 1 1 0 CEL; MAT; MAE clinical 0 United States of America Mexico
Language, Identity, and Behavioral Acculturation Scale Birman et al. (2002) 1 1 1 0 OWN refugee students 0 United States of America former Soviet Union
Level of Acculturation to Korean Life Asano et al. (2016) 1 1 1 0 OWN wife women 0 South Korea China
linguistic, psychological and behavioral indicators for acculturation Y. Shin & Maupome (2017) 0 1 1 0 OWN general 0 United States of America Mexico
Los Angeles Epidemiologic Catchment Area-Acculturation Scale Burnam et al. (1987) 0 1 0 0 MAE; WAL general 0 United States of America Mexico
Male Arab Acculturation scale (MAAS) D. T. Barry (2005) 1 1 1 0 OWN general 0 Arab United States of America
Measure of identification with groups Roccas et al. (2008) 1 1 1 0 OWN prospective jurors, student 1 United States of America any
Measurement of Acculturation Strategies for People of African Descent (MASPAD) Obasi & Leong (2010) 1 1 1 0 OWN general 0 United States of America African descent
Media Acculturation Scale A. G. Ramirez et al. (1986) 0 1 0 0 CEL; WAL; ZAN general 0 United States of America Mexico
Mental Health Continuum Short Form (MHC-SF) C. L. M. Keyes (2002) 1 0 1 0 OWN general 1 United States of America any
Mexican American Acculturation Scale Montgomery (1992) 1 1 1 0 MAE; WAL students 1 United States of America Mexico
Mexican American Cultural Values Scale (MACVS) Knight et al. (2010) 0 0 1 0 OWN youth 1 United States of America Mexico
Migrant Integration Indexes (Lindstorm, 2019) Lindstrom (2019) 0 1 0 0 OWN general 0 United States of America Mexico
minority and majority group contact (SIM) Maliepaard & Phalet (2012) 0 1 0 0 OWN general 0 Netherlands Marocco, Turkey
Modified Bicultural Involvement Questionnaire Johnson-Agbakwu et al. (2016) 1 1 1 1 OWN refugee women 0 United States of America Somalia
modified Lowlands Acculturation Scale Ünlü Ince et al. (2014) 1 1 1 1 OWN general 0 Netherlands Turkey
Motivation for Cultural Exploration (MCE) Recker et al. (2017) 1 1 1 0 OWN general 0 New Zealand any
Motivation for Cultural Maintenance (MCM) Recker et al. (2017) 1 1 1 1 OWN general 0 New Zealand any
Multicultural Acculturation Scale Wong-Rieger & Quintana (1987) 1 1 1 0 MAT general 1 United States of America Southeast Asia, LatinX
Multicultural Experience Survey A. K. yee Leung & Chiu (2010) 0 1 1 0 CEL students 1 United States of America United States of America
Multidimensional Acculturative Stress Inventory Rodriguez et al. (2002) 1 1 1 0 CEL general 0 United States of America Mexico
Multidimensional Acculturative Stress Scale Jibeen & Khalid (2010) 1 1 1 0 CEL general 0 Canada Pakistan
Multidimensional Measure of Cultural Identity Scales for Latinos and Latinas Felix-Ortiz et al. (1994) 0 1 1 0 WAL; MAE students 0 United States of America LatinX
Multigroup Ethnic Identity Measure (MEIM) Phinney (1992) 1 1 1 0 OWN students 0 United States of America any
Multigroup Ethnic Identity Measure–Revised Phinney & Ong (2007) 1 1 1 0 OWN students 1 United States of America any
Multiphasic Assessment of Cultural Constructs Cuéllar et al. (1995) 1 1 1 0 MAT; WAL students 0 United States of America Mexico
Mutual Intercultural Relations in Plural Societies - Acculturation Attitudes Scale (MIRIPS) Berry (2010) 0 0 1 1 CEL general 1 multiple multiple
Na Mea Hawai’i Scale Rezentes (1993) 0 1 1 0 CEL; MAT; ZAN general 1 United States of America Native
National Identity Fuller-Rowell et al. (2013) 1 0 1 0 OWN student 1 United States of America LatinX
Native American Acculturation Scale Garrett & Pichette (2000) 1 1 1 0 CEL; MAT N/A N/A United States of America Native
Pan-Acculturation Scale (Soriano, 1999) Soriano (1999) 1 1 1 0 OWN youth 0 United States of America LatinX
Perceived discrimination (ICSEY) ICSEY team (2006) 1 0 1 0 OWN youth 0 multiple multiple
Perceived Discrimination (MIRIPS) Berry (2010) 1 0 1 0 CEL general 1 multiple multiple
Perez Ambiguous Loss of Homeland Scale (PALH) Rose M. Perez & Arnold-Berkovits (2019) 1 0 1 1 OWN general 0 United States of America LatinX
Physical Activity and Sport Acculturation Index (PASAI) Garrido et al. (2012) 0 1 1 0 OWN youth 0 Spain any
Place Attachment Scale (Williams, & Vaske, 2003) Williams & Vaske (2003) 0 1 1 0 OWN students 1 United States of America United States of America
political participation (Jeong, & Kim, 2016) Jeong & Kim (2016) 0 1 1 0 OWN refugees 0 South Korea North Korea
Preferred and Perceived Acculturation Strategies Zagefka & Brown (2002) 0 0 1 0 OWN
Proxy Acculturation Scale (PAS-3 and PAS-4) T. H. Cruz et al. (2008) 0 1 0 0 OWN general 0 United States of America LatinX
Psychological Acculturation Scale (Tropp et al., 1999) Tropp et al. (1999) 1 0 1 0 CEL general 0 United States of America LatinX
Psychological adaptation - Life Satisfaction (ICSEY) ICSEY team (2006) 1 0 1 0 OWN youth 0 multiple multiple
Psychological adaptation - Psychological Problems (ICSEY) ICSEY team (2006) 1 0 1 0 OWN youth 0 multiple multiple
Psychological adaptation - Self Esteem (ICSEY) ICSEY team (2006) 1 0 1 1 OWN youth 0 multiple multiple
Psychological Adaptation (Demes, & Geeraert, 2014) Demes & Geeraert (2014) 1 0 0 0 OWN general, student 0 United Kingdom any
Psychological-Behavioral Acculturation Survey Mariño et al. (2000) 0 1 1 0 OWN general 1 Australia Vietnam
Relative Acculturation Extended Model Scale (real and ideal acculturation strategies and attitudes [RAEM]) Navas et al. (2007) 0 1 1 1 OWN general 1 Spain Morocco, Sub-Saharan Africa
Revised Multidimensional Inventory of Black Identity Sellers et al. (1997) 1 0 1 0 OWN students 1 United States of America African descent
Satisfaction With Migration Life Scale Félix Neto & Fonseca (2016) 1 0 1 1 OWN general 0 United States of America any
Scale of Acculturation Rissel (1997) 0 1 1 0 CEL; MAT general 0 Australia Arabic Speaking
Self-administered questionnaire of Acculturation Palmer et al. (2007) 1 1 1 0 OWN general 1 United Kingdom Indian, Pakistani, Bangladeshi
Self-Reported Life Satisfaction Paparusso (2019) 1 0 1 0 OWN general 0 Europe any
Short Acculturation Scale Wallen et al. (2002) 1 1 1 0 CEL; WAL women 0 United States of America Central America
Short Acculturation Scale for Filipino Americans F. A. D. Cruz et al. (2000) 0 1 1 1 MAT experts 0 United States of America Philippines
Short Acculturation Scale for Hispanic Youth Barona & Miller (1994) 0 1 1 0 CEL; MAE; WAL youth 1 United States of America Hispanic
Short Acculturation Scale for Hispanics — 12 items (Marin, Sabogal, Marin, Otero-Sabogal, & Perez-Stable, 1987) Marin et al. (1987) 0 1 1 1 CEL; MAE; MAT; WAL; ZAN general 1 United States of America Hispanic
Short Acculturation Scale for Hispanics — 5 items (Marin, Sabogal, Marin, Otero-Sabogal, & Perez-Stable, 1987) Marin et al. (1987) 0 1 1 0 CEL; MAE; MAT; WAL; ZAN general 1 United States of America Hispanic
Short Acculturation Scale for Hispanics-Youth (SASH-Y) Barona & Miller (1994) 0 1 1 0 OWN youth 1 United States of America LatinX
Social Connectedness in Mainstream Society (SCMN) Scale E. Yoon et al. (2012) 1 0 0 0 OWN students 0 United States of America Mexico, Asia
Social Connectedness in the Ethnic Community (SCETH) Scale E. Yoon et al. (2012) 1 0 0 0 OWN students 0 United States of America Mexico, Asia
social integration index (Dalgard, & Thapa, 2007) Dalgard & Thapa (2007) 0 1 1 0 OWN general 0 Norway any
Social markers of acculturation Leong (2014b) 0 1 0 0 OWN general 1 Singapore any
Social self-efficacy Scale for Students Fan & Mak (1998) 1 1 1 0 OWN students 1 Australia any
Socio-Cultural Adaptation Scale (29-item) Ward & Kennedy (1999) 0 1 1 0 OWN general, student 0 New Zealand, Singapore New Zealand, Singapore
Socio-Cultural Adaptation Scale (41-item) Ward & Kennedy (1999) 0 1 1 0 OWN general, student 0 New Zealand, Singapore New Zealand, Singapore
Sociocultural adaptation - Behavior problems (ICSEY) ICSEY team (2006) 0 1 0 0 OWN youth 0 multiple multiple
Sociocultural adaptation - School adjustment (ICSEY) ICSEY team (2006) 1 1 1 1 OWN youth 0 multiple multiple
Sociocultural Adaptation Scale Ward & Kennedy (1994) 0 1 1 0 CEL general 0 multiple New Zealand
Sociocultural Adaptation Scale (LSYPE) Bowe (2019) 1 1 1 1 OWN youth 0 United Kingdom any
sociocultural adaptation/perceived cultural distance (Demes, & Geeraert, 2014) Demes & Geeraert (2014) 0 1 1 0 OWN general, student 0 United Kingdom any
Sociocultural Adjustment Scale - Revised (SCAS-R) Wilson (2013) 0 1 1 0 OWN general 0 New Zealand any
Soviet Acculturation Measure (SAM-II) Ben-Rafael et al. (2006) 1 1 1 1 OWN Jews 0 Israel, Germany, United States of America Russia
Spheres of sociocultural adjustment Lissitsa & Peres (2011) 1 1 1 1 OWN general 1 Israel Russia
Stephenson Multigroup Acculturation Scale Stephenson (2000) 1 1 1 0 CEL; MAT; ZAN general 0 United States of America any
Strategies of Acculturation Scale Eshel & Rosenthal-sokolov (2000) 1 0 1 1 OWN youth 0 Israel Russia
Subjective Acculturation Measure Norris et al. (1996) 1 0 1 0 OWN general 0 United States of America LatinX
Suinn-Lew Asian Self-Identity Accultururation Scale (SL-ASIA) Suinn et al. (1992) 1 1 1 0 CEL; MAE; MAT; ZAN students 0 United States of America Asia
Taiwan Aboriginal Acculturation Scale Cheng & Hsu (1995) 1 1 1 1 MAT general 0 Taiwan East Asia & Pacific
Traditional Behavior Scale Arambula Solomon & Gottlieb (1999) 0 1 0 0 MAT general 0 United States of America Native
Trinity Acculturation Scale (14 items) Curran (2003) 1 1 1 1 OWN general 0 United Kingdom Ireland
Trinity Acculturation Scale (24 items) Curran (2003) 1 1 1 1 OWN general 0 United Kingdom Ireland
United States acculturation scale (USAS) A. A. Wright et al. (2013) 1 1 1 1 OWN clinical 1 United States of America any
Value Acculturation Scale Szapocznik et al. (1978) 0 0 1 0 MAE; MAT; ZAN general 1 United States of America Cuba
Vancouver Index of Acculturation (VIA; Ryder et al., 2000) Ryder et al. (2000) 1 1 1 0 CEL; MAT general 1 Canada China
Virgin Island Acculturation Scale (Tull, Ambrose, & Chambers, 2003) Tull et al. (2003) 0 0 1 1 OWN general 0 United States Virgin Islands African Cribbean
a CEL = Celenk & Van de Vijver (2011), MAE = Maestas (2000), MAT = Matsudaira (2006), WAL = Wallace et al. (2010), ZAN = Zane & Mak (2004), OWN = own review (only additional)

Empirical Literature

The 526 empirical works are listed in Table 4.

dt.Empirical.included %>%
  dplyr::select(Reference = CitationKey, 
                #Affect,    Behavior,   Cognition,  Desire,
                Affect = AffectFinal, Behavior = BehaviorFinal, Cognition = CognitionFinal, Desire = DesireFinal,
                `Publication Type` = PublicationType, Method, Sample,   `Majority Included` = IncludesMajority, `Migration Time` = MigrationTime,
                `Country of Settlement` = HostCountry,  `Country of Origin` = OriginCountry) %>%
  mutate_at(vars(Affect,    Behavior,   Cognition, Desire), ~replace_na(., 0)) %>%
  mutate(Reference = paste0("@",Reference)) %>%
  kbl(., caption = "Empirical Literature",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
   scroll_box(width = "110%", height = "750px")
Table 4: Empirical Literature
Reference Affect Behavior Cognition Desire Publication Type Method Sample Majority Included Migration Time Country of Settlement Country of Origin
Rania et al. (2019) 0 1 1 1 journalArticle quantitative general 1 post Italy any
Harder et al. (2018) 1 1 1 1 journalArticle quantitative general 0 post United States multiple
J. Yoon (2019) 0 1 1 0 thesis mixed method refugee 0 post United States Sudan
Haase et al. (2019) 0 1 1 0 journalArticle quantitative refugee 0 post Germany any
Hashemi et al. (2019) 1 1 1 0 journalArticle quantitative general 0 post Australia Middle East
Safdar et al. (2017) 0 0 1 1 bookSection quantitative general 0 post United States LatinX, China
Salo & Birman (2015) 1 1 1 0 journalArticle quantitative refugee 0 post United States Vietnam
Testa et al. (2019) 1 1 1 0 journalArticle quantitative general 0 post Canada, Italy any
Roblain et al. (2017) 0 0 1 0 journalArticle quantitative refugee men 0 post Belgium Syria, Iraq
Baranik et al. (2018) 0 1 1 0 journalArticle mixed method refugee 0 post United States any
Yakobov et al. (2019) 1 1 1 0 journalArticle quantitative general 0 post Canada Former Soviet Union
Nakash et al. (2015) 1 0 0 1 journalArticle quantitative refugee 0 post Israel Eritrea, Sudan
Jurcik et al. (2019) 1 1 1 0 journalArticle quantitative clinical 0 post Canada any
Eylem et al. (2019) 1 1 1 1 journalArticle quantitative general 0 post Netherlands Turkey
Sadeghi (2019) 0 1 1 1 thesis quantitative general 0 post United States Iran
Buckingham & Suarez-Pedraza (2019) 0 1 1 1 journalArticle quantitative general 0 post United States LatinX
Szabo et al. (2016) 0 1 1 1 journalArticle quantitative general 0 post New Zealand any
Roytburd & Friedlander (2008) 1 1 1 0 journalArticle quantitative refugee 0 post United States Former Soviet Union Jews
Benitez (2018) 1 0 1 0 thesis quantitative general 0 post United States Hispanic
Groen et al. (2019) 1 0 1 1 journalArticle mixed method refugee 0 post Netherlands Afghanistan, Iraq
Tatarko et al. (2020) 0 0 1 1 journalArticle quantitative general 0 post Russia South Korea, Uzbekistan, Tajikistan, Turkmenistan, Kyrgyzstan, Kazakhstan
M. Lee et al. (2018) 1 1 1 0 journalArticle quantitative general 0 post United States Korea
Maas et al. (2019) 0 1 1 0 journalArticle quantitative general 1 post Netherlands any
Repke & Benet-Martínez (2018) 1 1 1 0 journalArticle quantitative general 0 post Spain Moroccan, Pakistani, Ecuadorian, Romanian
Zagefka & Brown (2002) 0 0 1 0 journalArticle qualitative youth 1 post Germany any
Gore (2019) 0 1 1 0 thesis quantitative youth 0 post United States LatinX
Abbas et al. (2018) 0 1 1 0 journalArticle quantitative religious 0 post Australia Muslims
Dellaserra et al. (2018) 1 1 1 0 journalArticle quantitative general 0 post United States Mexico
Bostean & Gillespie (2018) 1 1 1 0 journalArticle quantitative general 0 post United States LatinX
Haverkamp et al. (2016) 1 1 1 1 journalArticle quantitative clinical 0 post Netherlands any
Spasojević et al. (2000) 0 1 0 1 journalArticle quantitative refugee 0 post United States Bosnia
Aycan & Berry (1996) 1 1 1 1 journalArticle quantitative general 0 post Canda Turkey
Yang & Yang (2018) 0 1 0 0 journalArticle quantitative youth 0 post United States China
Mariño et al. (2000) 0 1 1 1 journalArticle quantitative general 1 post Australia Vietnam
Logan et al. (2017) 0 1 1 0 journalArticle quantitative general 0 post United States Korea
M. H. Nguyen et al. (2017) 1 1 1 0 journalArticle quantitative clinical 0 post Germany Vietnam
Cárdenas & Sablonnière (2017) 1 1 1 0 journalArticle quantitative general 0 post Canada LatinX
Madjlessi (2017) 0 1 1 1 thesis quantitative general 0 post United States Iran
Moztarzadeh & O’Rourke (2015) 0 1 1 1 journalArticle quantitative elderly 0 pre & post Canada Iran
J. H. Lee (2017) 1 1 1 0 thesis quantitative general 0 post United States Korea
Siwiec (2012) 1 1 1 0 thesis quantitative refugee 0 post United States Bosnia
Meca et al. (2017) 1 0 1 0 journalArticle quantitative undocumented migrant 0 post United States LatinX
Akram-Pall & Moodley (2016) 1 0 1 0 journalArticle qualitative general 0 post Canada India, Pakistan
Murray (2010) 1 1 1 0 journalArticle mixed method refugee 0 post Australia Sudan
Behrens et al. (2015) 1 1 1 1 journalArticle quantitative clinical 0 post Germany any
Khawaja et al. (2016) 1 1 1 0 journalArticle quantitative general 0 post Australia Taiwan
Vinokurov (2001) 1 1 1 0 thesis mixed method general 0 post United States Former Soviet Union
Geltman et al. (2014) 0 0 1 0 journalArticle qualitative refugee 0 post United States Somalia
Maehler et al. (2019) 1 1 1 0 journalArticle quantitative general 1 post Germany any
Birman & Tran (2008) 1 1 1 0 journalArticle quantitative refugee 0 post United States Vietnam
N. M. Lebedeva & Tatiana A. (2016) 0 0 1 0 journalArticle quantitative general 1 post Russia Uzbekistan, Tajikistan
Dinut (2014) 1 1 1 0 thesis quantitative general 0 post United States Romania
Cobb et al. (2017) 1 1 1 0 journalArticle quantitative undocumented migrant 0 post United States LatinX
De Leersnyder (2017) 1 0 0 0 journalArticle Review N/A 0 post N/A N/A
Zlotnick et al. (2019) 0 1 1 0 journalArticle quantitative general 0 post Canada, South Africa, UK, United States Israel
Niejenhuis et al. (2018) 1 0 1 0 journalArticle quantitative youth 0 post Netherlands any
Migliorini et al. (2016) 0 1 1 0 journalArticle quantitative general 1 post Italy any
Donà & Berry (1994) 0 0 1 0 journalArticle quantitative refugee 0 post Canada Central America
Schmitz & Schmitz (2012) 1 0 1 0 journalArticle quantitative general 0 post Germany any
Jibeen (2011) 1 0 1 0 journalArticle quantitative general 0 post Canada Pakistan
Green et al. (2014) 0 0 1 0 journalArticle quantitative general 0 post France Senegal
S. Y. Park et al. (2014) 0 1 0 0 journalArticle quantitative general 0 post United States China, Philippines, Vietnam
Ferenczi & Marshall (2016) 1 1 1 1 journalArticle quantitative general 0 post United Kingdom any
Nourian (2012) 0 1 1 1 thesis quantitative general 0 post United States Iran
Ramelli et al. (2013) 1 0 1 0 journalArticle quantitative general 0 post Switzerland, Italy spanish speaking
Cernovsky (1990a) 1 1 1 1 journalArticle quantitative refugee 0 post Switzerland Czechoslovacia
Cernovsky (1990b) 1 1 1 1 journalArticle quantitative refugee 0 post Switzerland Czechoslovacia
Ramdhonee & Bhowon (2012) 1 1 1 0 journalArticle quantitative marital partner 0 post Mauritius any
López-Rodríguez et al. (2016) 0 0 1 0 journalArticle quantitative general 0 post Spain Marocco, Ecuador
Spanea & Kalantzi-Azizi (2008) 0 0 1 0 journalArticle quantitative general 1 post Greece any
Cuadrado et al. (2018) 0 1 1 0 journalArticle quantitative general 0 post Spain Romania
Vaswani et al. (2019) 1 1 1 0 journalArticle quantitative general 0 post Canada LatinX
Sekhon (2008) 1 1 1 0 thesis quantitative refugee 0 post United States Somalia
Bharmal et al. (2014) 0 1 1 0 journalArticle quantitative general 0 post United States India
Capasso et al. (2018) 0 0 1 0 journalArticle quantitative general 0 post Italy any
Gonidakis et al. (2013) 0 1 1 1 journalArticle quantitative clinical 0 post Greece any
Yijälä & Jasinskaja-Lahti (2010) 0 0 1 0 journalArticle quantitative general 0 potential Finland Russia
Khawaja et al. (2019) 1 1 1 0 journalArticle quantitative refugee 0 post Australia Ethiopia, Congo, Myanmar
Ford et al. (2014) 0 1 0 0 journalArticle quantitative general 0 post Thailand Myanmar, Cambodia, Laos
Safdar et al. (2012) 0 0 1 0 journalArticle quantitative general 0 post Canada India, Russia
Hanke et al. (2017) 0 0 1 0 bookSection quantitative general 0 post Germany Greece
im et al. (2009) 0 1 0 0 journalArticle quantitative clinical 0 post United States Hispanic, Asian
Leersnyder et al. (2011) 1 1 1 0 journalArticle quantitative general 1 post United States, Belgium Korea, Turkey
Szabo & Ward (2015) 0 1 1 1 journalArticle quantitative general 0 post any any
Sankey (2018) 1 1 1 0 thesis quantitative general 0 post United States Central America
Atri et al. (2011) 1 1 1 0 journalArticle quantitative students 0 post United States any
Moran (2014) 0 1 1 1 thesis quantitative marital partner 0 post United States any
Willems (2013) 1 1 1 0 thesis quantitative muslim 0 post United States Arabs
Güngör et al. (2013) 1 1 1 0 journalArticle quantitative general 1 post United States Japan
Abu-Bader et al. (2011) 1 1 1 0 journalArticle quantitative elderly 0 post United States Muslims
Leong (2014a) 0 1 0 0 journalArticle quantitative general 1 post Singapore any
Ellison et al. (2011) 0 1 1 1 journalArticle quantitative general 0 post United States Hispanic
Tahseen & Cheah (2012) 0 0 1 0 journalArticle quantitative mothers 0 post United States China
G. Kim et al. (2012) 0 1 0 0 journalArticle quantitative elderly 0 post United States Korea
Chavez-Payan et al. (2015) 0 1 0 0 journalArticle quantitative youth 0 post United States Hispanic
D. T. Barry (2005) 1 1 1 0 journalArticle quantitative men 0 post United States Arabs
Herrero et al. (2011) 1 0 1 0 journalArticle quantitative general 0 post Spain LatinX
Copelj & Kiropoulos (2011) 1 1 1 0 journalArticle quantitative general 1 post Australia former Yugoslavia
K. T. Wang et al. (2012) 1 0 0 0 journalArticle quantitative students 0 pre & post United States China
Ea et al. (2010) 0 1 1 1 journalArticle quantitative nurses 0 post United States, Israel Philippines, former Soviet Union
Rubin et al. (2012) 1 1 0 0 journalArticle quantitative general 0 post Australia any
Helms et al. (2014) 1 1 1 0 journalArticle quantitative general 0 post United States Mexico
Shim & Schwartz (2008) 1 1 1 0 journalArticle quantitative general 0 post United States Korea
Cordero & Gutierrez (2016) 1 1 1 0 journalArticle quantitative student women 0 post United States LatinX
Sundram et al. (2008) 0 0 1 0 journalArticle quantitative clinical 1 post Australia any
C. K. Y. Chan & Glynn Owens (2006) 1 1 1 0 journalArticle quantitative students 0 post New Zealand China
Udahemuka & Pernice (2010) 0 0 1 1 journalArticle quantitative general 0 post New Zealand Africa
C. K. Y. Chan et al. (2010) 1 1 1 0 journalArticle quantitative general 0 post New Zealand Korea
Sussman & Truong (2011) 1 1 1 0 journalArticle quantitative general 0 post United States China, Russia
Saissy (2009) 1 1 1 0 journalArticle quantitative general 1 post Northern Ireland China
Madianos et al. (2008) 0 1 1 1 journalArticle mixed method general 0 post Greece any
Bourhis et al. (2009) 0 0 1 0 journalArticle quantitative students 1 post United States any
De Leersnyder et al. (2013) 1 0 0 0 bookSection quantitative general 1 post Belgium Turkey
Lissitsa & Peres (2011) 1 1 1 1 journalArticle mixed method general 1 post Israel Russia
Jibeen & Khalid (2010) 1 0 1 0 journalArticle quantitative general 0 post Canada Pakistan
E. Kateri & Karademas (2009) 0 1 1 0 journalArticle quantitative general 0 post Greece India, Albania
Rhee (2017) 1 1 1 0 journalArticle quantitative elderly 0 post United States Korea
Abdulahad (2012) 1 1 1 1 thesis quantitative general 0 post Canada Iraq
Ward & Kus (2012) 0 1 1 0 journalArticle quantitative general 0 post New Zealand “visibly different” immigrants
Safdar et al. (2009) 0 1 1 0 journalArticle quantitative general 0 post United States, Netherlands, United Kingdom Iran
Kosic et al. (2004) 1 1 1 0 journalArticle quantitative general 0 post Italy Croatia, Poland
Tharmaseelan et al. (2010) 1 1 0 0 journalArticle quantitative general 0 post New Zealand Sri Lanka
S. X. Chen et al. (2008) 1 0 1 0 journalArticle quantitative general 1 post Hong Kong China, Phillipines
Mo et al. (2006) 0 1 1 0 journalArticle quantitative women 0 post Hong Kong China
Lora et al. (2011) 0 1 1 0 journalArticle quantitative women 0 post United States LatinX
Navas et al. (2007) 0 1 1 1 journalArticle quantitative general 1 post Spain Marocco, Sub-Saharan Africa
Luque et al. (2006) 0 1 1 1 journalArticle quantitative general 0 post Spain Marocco, Sub-Saharan Africa
E. F. Keyes & Kane (2004) 1 1 1 1 journalArticle qualitative refugee women 0 post United States Bosnia
Buddington (2002) 0 1 0 1 journalArticle quantitative students 0 post United States Jamaica
Kosic (2002) 1 1 1 0 journalArticle quantitative general 0 post Italy Poland, Croatia
Chung et al. (2000) 1 1 1 0 journalArticle quantitative refugee students 0 post United States Vietnam
Kurman et al. (2005) 1 0 1 1 journalArticle quantitative students 0 post Israel Russia, Ethiopia
Mokounkolo & Taillandier-Schmitt (2008) 1 1 1 0 journalArticle quantitative general 0 post France North Africa
Cheung (1995) 1 1 1 0 journalArticle quantitative general 0 post New Zealand Cambodia
Oerlemans & Peeters (2010) 0 1 1 0 journalArticle quantitative general 1 post Netherlands non-western
Nesdale & Mak (2000) 0 0 1 1 journalArticle quantitative general 0 post Australia Vietnam, China, Hong Kong, Taiwan, Sri Lanka, New Zealand
Au et al. (1998) 0 1 0 0 journalArticle quantitative general 0 post United States China
Zlobina et al. (2006) 0 1 0 0 journalArticle quantitative general 0 post Spain Brazil, Colombia, Ecuador, North Africa, Sub-Saharan Africa
Arends-Tóth & Vijver (2007) 0 1 1 0 journalArticle quantitative general 0 post Netherlands Turkey
X. Gao et al. (2014) 0 1 0 0 journalArticle quantitative foreign domestic workers 0 post Hong Kong Indonesia
Jang & Chiriboga (2010) 0 1 1 0 journalArticle quantitative elderly 0 post United States Korea
Knipscheer et al. (2000) 1 1 1 1 journalArticle quantitative general 0 post Netherlands Ghana
Suanet & Van de Vijver (2009) 0 1 1 0 journalArticle quantitative students 0 post Russia any
W. N. Lee & Tse (1994) 0 1 1 0 journalArticle quantitative general 0 post Canada Hong Kong
D. Barry (2001) 1 1 1 0 journalArticle quantitative general 0 post United States China, Japan, Korea
Sharp (2010) 1 1 1 0 thesis quantitative general 0 post United States Mexico
Sung (2011) 0 0 1 0 thesis quantitative general 0 post United States Korea
Wen Chen et al. (2017) 0 1 1 0 journalArticle quantitative refugee 0 post Australia any
Van Selm et al. (1997) 0 0 1 1 journalArticle quantitative refugee 0 post Norway Bosnia
Pham & Harris (2001) 0 1 1 1 journalArticle quantitative general 0 post United States Vietnam
Al-Sabiae & Di Nicola (1995) 0 1 1 0 journalArticle quantitative general 0 post Canda Saudi Arabia
Curran et al. (2002) 1 1 1 1 journalArticle quantitative general 0 post Great Britain Ireland
Ben-Shalom & Horenczyk (2003) 1 0 1 1 journalArticle quantitative soldiers 0 post Israel former Soviet Union
Franzini & Fernandez-Esquer (2004) 0 1 0 0 journalArticle quantitative general 0 post United States Mexico
Tull et al. (2003) 0 1 1 0 journalArticle quantitative general 0 post Virgin Islands African Cribbean
Barrette et al. (2004) 0 1 1 0 journalArticle quantitative students 1 post France North Africa
Alkhiary (2015) 0 0 1 0 thesis quantitative general 0 post United States Saudi Arabia
Alegria et al. (2007) 0 1 0 0 journalArticle quantitative general 1 post United States LatinX
Zane & Mak (2004) 0 1 1 0 bookSection Review N/A 0 post any amy
Torres & Rollock (2004) 1 1 1 1 journalArticle quantitative general 0 post United States Hispanics
Kashima & Loh (2006) 1 1 1 0 journalArticle quantitative students 0 post Australia Asia
Galchenko & Vijver (2007) 0 1 1 0 journalArticle quantitative students 0 post Russia any
Henkin et al. (2011) 1 1 1 0 journalArticle quantitative general 0 post United States Puerto Rico
Noda et al. (1990) 1 0 0 0 journalArticle quantitative women 0 post Canada Japan
Gonzalez-Castro & Ubillos (2011) 1 1 1 0 journalArticle quantitative general 0 pre & post Spain Romania, Ecuador
Kushnirovich & Youngmann (2017) 1 1 1 0 journalArticle quantitative general 0 post Israel any
J. Y. An et al. (2016) 0 1 1 1 journalArticle quantitative nurses 0 post United States Korea
C. Kim et al. (2001) 1 1 1 0 journalArticle quantitative general 0 post Canada Italy
Rose (2009) 1 1 1 0 thesis mixed method general 0 post United States LatinX, Spain
Azar (2009) 1 1 1 0 thesis quantitative general 0 post United States Arabs
Scott & Scott (1985) 1 1 1 1 journalArticle qualitative general 0 pre & post Australia any
Stodolska (2000) 0 1 0 1 journalArticle mixed method general 0 post Canada Poland
Aqtash & Van Servellen (2013) 1 1 1 0 journalArticle quantitative general 0 post United States Jordan, Syria, Lebanon, Palestine
Schall (2008) 1 1 1 0 thesis quantitative general 0 post United States LatinX
Kallampally (2005) 1 1 1 0 thesis quantitative women 0 post United States India
T. K. Le (2012) 1 1 1 0 thesis mixed method siblings 0 post Canada Vietnam, China
Matsumoto et al. (2001) 1 1 1 0 journalArticle quantitative general 0 post United States Japan
Kassabian (2003) 1 1 1 0 thesis quantitative general 0 post United States Armenia
Evans & Stead (2014) 1 1 1 0 journalArticle qualitative athletes 0 pre & post United Kingdom Australasia
M. Lu et al. (2017) 1 1 1 0 journalArticle quantitative general 0 post Canada, United States any
Okahaabwa (2015) 0 1 1 0 thesis quantitative general 0 post Canada, United States Uganda, Kenya
N. Lebedeva & Tatarko (2013) 0 0 1 0 journalArticle Review general 1 post Russia any
Félix Neto & Fonseca (2016) 0 0 1 1 journalArticle quantitative general 0 post United States any
Handojo (2000) 1 1 1 0 thesis quantitative general 0 post United States Indonesia
Li et al. (2014) 1 1 1 0 journalArticle quantitative general 0 post United States China
N. M. Lebedeva et al. (2016) 0 1 1 0 journalArticle quantitative general 1 post Russia, Latvia any, Russia
Becker (1990) 1 1 1 1 journalArticle qualitative students 1 post United States Portugal
Guest & Stamm (1993) 0 1 0 0 journalArticle quantitative general 0 post United States any
Tinghög et al. (2010) 0 1 1 0 journalArticle quantitative general 0 post Sweden Finland, Iraq, Iran
Buford (2011) 0 1 1 0 thesis qualitative general 0 post United States Ghana, Nigeria
Kimhi & Bliwise (1992) 0 0 1 1 journalArticle quantitative general 0 post United States Israel
Nortey (2005) 1 1 1 0 thesis quantitative general 0 post United States non-western
Bhatnagar (1980) 0 1 0 0 journalArticle quantitative children 0 post Canada Italy
E. Yoon (2006) 1 1 1 0 thesis quantitative general 0 post United States Korea
Abrahams (2007) 1 1 1 0 thesis quantitative general 0 post United States India
J. Park & Roh (2013) 1 1 1 0 journalArticle quantitative elderly 0 post United States Korea
Zhou (2012) 1 1 0 0 journalArticle qualitative general 0 post Canada China
Polek et al. (2008) 1 1 1 0 journalArticle quantitative general 0 post Netherlands Poland, Russia, Hungary
Yao (1979) 1 1 1 0 journalArticle quantitative general 0 post United States China
Wiley (2013) 1 0 1 0 journalArticle quantitative general 0 post United States Mexico, Dominican Republic
Mier et al. (2012) 0 1 1 0 journalArticle quantitative elderly 0 post United States Hispanics
Merideth (2013) 1 1 1 0 thesis quantitative general 0 post United States Mexicos
D. Barry (2001) 1 1 1 0 journalArticle quantitative general 0 post United States East Asian
Masuda et al. (1970) 1 1 1 0 journalArticle quantitative general 0 post United States Japan
Vignes & Hall (1979) 1 1 1 0 journalArticle mixed method general 0 post United States Vietnam
Huang & Spurgeon (2006) 1 0 1 0 journalArticle qualitative general 0 post Britain China
Moghaddam et al. (1987) 0 1 1 0 journalArticle quantitative general 0 post Canada Iran
Ayçiçegi-Dinn & Caldwell-Harris (2011) 0 0 1 0 journalArticle quantitative general 0 post United States Turkey
Thomas (2008) 0 1 1 0 thesis quantitative general 0 post United States West Africa
Miglietta & Tartaglia (2009) 1 0 1 0 journalArticle quantitative general 0 post Italy Latin America, North Africa, Romania
Swami (2009) 0 1 0 0 journalArticle quantitative students 0 post Britain Malaysia, China
Ataca & Berry (2002) 0 1 1 0 journalArticle quantitative general 0 post Canada Turkey
M. T. Kim et al. (2005) 0 1 0 0 journalArticle quantitative general 0 post United States Korea
Liou & Contento (2001) 1 1 1 0 journalArticle quantitative general 0 post United States China
Lavee & Krivosh (2012) 1 1 1 0 journalArticle quantitative Jewish 0 post Israel Soviet Union
Pilkington et al. (2012) 1 1 1 0 journalArticle quantitative muslim 0 post Britain South Asia
H. Y. Lee et al. (2004) 1 1 1 0 journalArticle quantitative general 0 post United States Korea
Shalom & Horenczyk (2004) 1 1 1 1 journalArticle quantitative military 0 post Israel Soviet Union
Jang et al. (2006) 0 1 1 0 journalArticle quantitative old adults 0 post United States Korea
McCaffrey (2008) 1 1 0 0 journalArticle qualitative elderly 0 post United States Haiti
Cuellar et al. (2004) 0 1 1 0 journalArticle quantitative old adults 0 post United States Mexico
Saghafi (2009) 1 1 1 1 thesis quantitative general 0 post United States Iran
C. Leung & Moore (2003) 0 1 1 0 journalArticle quantitative several age groups 0 post Australia China
Markovitzky & Mosek (2005) 1 1 1 0 journalArticle quantitative
Shishkin (2010) 0 0 1 0 thesis mixed method several age groups 0 post United States Russia
Roh et al. (2011) 0 1 1 0 journalArticle quantitative elderly 0 post United States Korea
Mjelde-Mossey et al. (2005) 1 1 0 0 journalArticle mixed method elderly 0 post China China
Chirkov et al. (2008) 0 1 0 0 journalArticle quantitative students 0 pre & post Canada Many
Reyes (1995) 1 1 1 1 thesis quantitative
O. K. Hong (1997) 1 0 1 0 thesis quantitative elderly 0 post United States Korea
Masuda (1980) 0 1 0 0 journalArticle mixed method general 0 post United States Vietnam
Valdes & Baxter (1976) 0 1 0 0 journalArticle quantitative general 0 post United States Cuba
Aycan (1997) 0 0 1 0 thesis quantitative general 0 post Canada Turkey
Jang et al. (2007) 0 1 1 0 journalArticle quantitative elderly 0 post United States Korea
Guo et al. (2018) 0 1 1 0 journalArticle quantitative elderly 0 post United States China
Recker et al. (2017) 1 1 1 1 journalArticle quantitative general 0 post New Zealand any
Bulut & Gayman (2020) 0 0 1 0 journalArticle quantitative general 0 post United States LatinX
Birman et al. (2014) 1 1 1 0 journalArticle quantitative refugee 0 post United States Russia
Froehlich et al. (2020) 1 0 1 0 journalArticle quantitative youth 0 post Germany any
Andrews et al. (2013) 1 1 1 0 journalArticle quantitative general 0 post United States LatinX
Coatsworth et al. (2005) 1 0 0 1 journalArticle quantitative youth 0 post United States Hispanic
Hennessy-Burt et al. (2011) 1 1 1 0 journalArticle quantitative general 0 post United States Mexico
Jasinskaja-Lahti & Liebkind (2007) 0 1 1 0 journalArticle quantitative general 0 post Finland former Soviet Union
Hsiao & Wittig (2008) 1 1 1 0 journalArticle quantitative youth 1 post United States Asia, LatinX
Motti-Stefanidi et al. (2008) 1 1 1 1 journalArticle quantitative youth 0 post Greece former Soviet Union
Fuentes-Afflick & Hessol (2008) 0 1 1 0 journalArticle quantitative pregnant women 0 post United States LatinX
Van Wieren et al. (2011) 0 1 0 0 journalArticle quantitative men 1 post United States LatinX
Ike-Chinaka (2013) 1 1 1 0 thesis quantitative youth 0 post United States Nigeria
Segev et al. (2014) 1 1 1 0 journalArticle quantitative general 0 post United States, Israel LatinX, Former Soviet Union
Tseng et al. (2015) 0 1 1 0 journalArticle quantitative women 0 post United States China
De Souza & Fuller-Thomson (2013) 0 1 0 0 journalArticle quantitative elderly 0 post United States Phillipines
Nicolaou et al. (2006) 0 1 0 0 journalArticle quantitative general 1 post Netherlands Surinam
Areba et al. (2021) 0 1 0 0 journalArticle quantitative youth 0 post United States Somalia, Hmong, LatinX
Rodríguez-Reimann et al. (2004) 1 1 1 0 journalArticle quantitative general 0 post United States Mexico
Y. I. Cho et al. (2013) 0 1 1 0 journalArticle mixed method general 0 post United States any
Ruesch et al. (1948) 1 1 1 1 journalArticle mixed method N/A 0 post United States any
Guevarra et al. (2005) 1 1 1 0 journalArticle quantitative women 0 post United States Africa
Yağmur & Vijver (2012) 1 1 1 1 journalArticle quantitative general 0 post Australia, France, Germany, Netherlands Turkey
Vella et al. (2011) 0 1 1 0 journalArticle quantitative women 0 post United States Mexico
Maehler et al. (2019) 0 0 1 0 journalArticle quantitative general 0 post Germany any
Janssen-Kallenberg et al. (2017) 0 0 1 0 journalArticle quantitative general 0 post Germany Turkey
D. Nguyen (2011) 0 0 1 0 journalArticle quantitative elderly 0 post United States Asia
Ying et al. (2012) 0 1 1 0 journalArticle quantitative women 0 post United States China
Fassaert et al. (2011) 1 1 1 1 journalArticle quantitative general 0 post Netherlands Marocco, Turkey
Fernandez & Loukas (2014) 0 1 1 0 journalArticle quantitative youth 0 post United States Mexico
Kovacev & Shute (2004) 1 1 1 1 journalArticle quantitative refugee students 0 post Australia Yugoslavia
Koydemir (2013) 1 1 1 0 journalArticle quantitative youth 0 post Germany Turkey
Eshun (2006) 0 1 1 0 journalArticle quantitative general 0 post United States Ghana
A. M. Ali (2008) 1 1 1 0 thesis mixed method general 0 post United States Somalia
Papaneophytou (2013) 1 1 1 0 thesis quantitative general 0 post United States Greece
T. N. Le et al. (2009) 0 1 1 0 journalArticle quantitative youth 0 post United States Cambodia, China, Laos, Vietnam
T. Tsai et al. (2011) 0 1 1 0 journalArticle mixed method women clinical 0 post United States China
Valente & Berry (2017) 0 1 0 0 journalArticle quantitative men 1 post United States LatinX
Dey & Sitharthan (2017) 0 0 1 1 journalArticle quantitative youth 0 post Australia India
Rojas et al. (2014) 0 0 1 0 journalArticle quantitative general 1 post Spain Romania
Mancini & Bottura (2014) 0 1 1 1 journalArticle quantitative youth 1 post Spain any
López-Rodríguez et al. (2014) 0 1 0 1 journalArticle quantitative youth 1 post Spain any
Needham et al. (2018) 0 1 0 1 journalArticle quantitative general 0 post United States South Asia
Y. J. Cho et al. (2018) 0 1 1 0 journalArticle quantitative women 0 post Korea Vietnam
Goforth et al. (2014) 1 1 1 0 journalArticle quantitative muslim youth 0 post United States Arabs
Johnson (2011) 0 1 0 0 journalArticle quantitative general 1 post United States Hispanic
Hilario et al. (2014) 0 1 0 0 journalArticle quantitative youth 0 post Canada Southeast Asia
Guler & Berman (2019) 1 0 1 0 journalArticle quantitative refugee youth 0 post United States any
Romano et al. (2005) 0 1 0 0 journalArticle quantitative general 1 post United States any
Lommel et al. (2019) 0 1 0 0 journalArticle quantitative general 1 post United States Mexico
Seokho Hong (2015) 0 1 1 0 thesis quantitative general 0 post United States China, Japan, Korea, Philippines, Vietnam
G. M. González Patricia; González (2008) 1 1 1 0 journalArticle quantitative general 1 post United States Mexico
Nilsson et al. (2008) 0 0 1 0 journalArticle quantitative refugee women 0 post United States Somalia
Tonsing et al. (2016) 1 1 1 0 journalArticle quantitative general 0 post United States Pakistan, Nepal
Trickett & Birman (2005) 1 1 1 0 journalArticle quantitative refugee youth 1 post United States former Soviet Union
E. Yoon et al. (2008) 1 0 1 0 journalArticle quantitative general 0 post United States Korea
X. V. Nguyen (2014) 1 1 1 0 thesis quantitative general 0 post United States Vietnam
Reitzel et al. (2010) 0 1 0 0 journalArticle quantitative smokers 0 post United States LatinX
Preciado & D’Anna-Hernandez (2017) 1 1 1 0 journalArticle quantitative women 0 post United States Mexico
Johnson-Agbakwu et al. (2016) 1 1 1 1 journalArticle quantitative refugee women 0 post United States Somalia
Onyigbuo et al. (2018) 1 1 1 0 journalArticle quantitative general 0 post United Kingdom Nigeria
Bozdağ & Bilge (2021) 1 1 1 0 journalArticle quantitative refugee 0 post Turkey Syria
Y. Choi et al. (2016) 0 1 1 0 journalArticle quantitative youth 0 post United States Korea
R. S. Wright (2013) 1 1 1 0 thesis quantitative general 0 post United States African-Caribbean
Peek et al. (2010) 0 1 1 0 journalArticle quantitative general 1 post United States Mexico
Liu (2006) 1 0 1 1 journalArticle quantitative general 0 post Australia China
Tam & Freisthler (2015) 0 1 1 0 journalArticle quantitative youth 0 post United States Southeast Asia
Mana et al. (2009) 1 1 1 1 journalArticle quantitative general 1 post Israel Ethiopia, Russia
Koca & La (2014) 0 1 1 0 journalArticle quantitative general 0 post Germany, United Kingdom Turkey
Segovia & Lufin (2013) 0 1 1 0 journalArticle qualitative general 0 post Chile Afro-Colombian
Son (2013) 1 1 1 0 journalArticle mixed method women 0 post United States Korea
López et al. (2016) 0 1 1 0 journalArticle quantitative general 0 post United States LatinX
Stoessel et al. (2014) 0 0 1 0 journalArticle quantitative youth 0 post Germany Russia
Akresh et al. (2014) 0 1 1 0 journalArticle quantitative general 0 post United States any
Kang et al. (2009) 1 0 1 0 journalArticle quantitative drug users 0 post United States Puerto Rico
E. Kim et al. (2010) 1 1 1 0 journalArticle quantitative general 0 post United States Korea
Hjellset & Ihlebæk (2019) 0 1 1 0 journalArticle quantitative women 0 post Norway Pakistan
McCullough et al. (2020) 1 1 1 0 journalArticle quantitative youth 0 post United States any
Martinovic et al. (2009) 0 1 0 0 journalArticle quantitative general 0 post Netherlands any
Safdar et al. (2020) 0 1 1 1 journalArticle mixed method general 1 post Canada China, Korea, India
Johnson-Kozlow (2010) 0 1 0 0 journalArticle quantitative general 0 post United States Mexico
Duarte (2020) 0 0 1 0 thesis quantitative general 1 post United States Mexico
Mann et al. (2017) 1 1 1 0 journalArticle mixed method sikh women 0 post United States India
Leu et al. (2011) 0 0 1 0 journalArticle quantitative general 0 post United States Asia
Rivera (2007) 0 1 1 0 journalArticle quantitative youth 0 post United States LatinX
Fang et al. (2016) 1 1 1 0 journalArticle quantitative general 0 post United States China
Fang (2016) 1 1 1 0 thesis quantitative general 0 post United States China
W. Wang & Yu (2015) 1 1 1 0 journalArticle quantitative general 0 post United States China
W. Y. Chan & Birman (2009) 1 1 1 0 journalArticle quantitative youth 0 post United States Vietnam
McKay-Semmler & Kim (2014) 1 1 1 0 journalArticle quantitative youth 0 post United States LatinX
Schachner et al. (2016) 1 1 1 0 journalArticle quantitative youth 1 post Germany any
H. Ngo & Li (2016) 1 1 1 0 journalArticle quantitative general 0 post Hong Kong China
Ryou et al. (2019) 0 1 1 0 journalArticle quantitative youth 0 post Korea any
Schiefer et al. (2012) 0 0 1 0 journalArticle quantitative general 1 post Israel; Germany former Soviet Union, Turkey, Arab
Ward & Styles (2005) 1 1 1 1 journalArticle mixed method women 0 pre & post Australia Britain
Kukaswadia et al. (2016) 1 0 1 0 journalArticle quantitative general 1 post Canada any
Afable-Munsuz et al. (2013) 0 1 0 0 journalArticle quantitative elderly 0 post United States Mexico
Dondero et al. (2018) 0 1 0 0 journalArticle quantitative youth 1 post United States Mexico
Akresh (2007) 0 1 1 0 journalArticle mixed method general 0 post United States any
Ellis et al. (2010) 0 1 0 0 journalArticle mixed method refugee youth 0 post United States Somalia
Araújo Dawson (2009) 0 1 1 0 journalArticle quantitative women 0 post United States Dominican Republic
Fu & VanLandingham (2012) 0 1 1 0 journalArticle quantitative general 0 pre & post United States Vietnam
Qian et al. (2018) 0 1 0 0 journalArticle quantitative general 1 post United States LatinX
Jian (2012) 0 1 1 0 journalArticle quantitative general 0 post United States any
Zhang et al. (2015) 0 1 0 0 journalArticle quantitative general 1 post United States LatinX
Sahni (2009) 1 1 1 1 thesis quantitative general 0 post United States, Canada India
Yarova et al. (2013) 1 1 1 0 journalArticle mixed method elderly women 1 post United States former Soviet Union
Kao & An (2012) 0 1 1 0 journalArticle quantitative general 0 post United States Mexico
Singhal (2005) 1 1 1 0 thesis quantitative women 0 post United States India
H.-H. Chen & Chien (2018) 0 1 1 0 journalArticle quantitative husband men 0 post Taiwan any
Persky & Birman (2005) 1 0 1 0 journalArticle quantitative Jewish refugee 0 post United States former Soviet Union
Casanova (2011) 0 1 1 0 thesis mixed method Yucatec Maya youth 0 post United States Mexico
Badea et al. (2015) 0 1 1 0 journalArticle quantitative general 0 post France Marocco
O’Connor et al. (2015) 1 1 1 0 journalArticle quantitative general 0 post United States LatinX
K. T.-K. Chan (2013) 0 0 1 0 thesis quantitative elderly 0 post United States Asia
Lineberry (2012) 0 1 1 0 thesis quantitative expatriates 0 post any United States
Benner & Kim (2009) 1 1 1 0 journalArticle quantitative youth 0 post United States China
Ferenczi & Marshall (2013) 1 1 1 0 journalArticle quantitative general 0 post any any
Constantine et al. (2010) 0 1 1 0 journalArticle mixed method general 0 post United States Southeast Asia
Plascencia (2014) 0 1 1 0 thesis quantitative students 0 post United States LatinX
Asano et al. (2016) 1 1 1 0 journalArticle quantitative wife women 0 post Korea China
El Khoury (2019) 1 1 1 0 journalArticle quantitative refugee 0 post Germany Syria
Maty et al. (2011) 0 1 1 0 journalArticle quantitative general 0 post United States China, Korea, Vietnam
Eisenberg et al. (2019) 0 1 0 0 journalArticle quantitative youth 1 post United States LatinX, Hmong, Somalia
Jasini et al. (2018) 1 0 0 0 journalArticle quantitative youth 1 post Belgium any
Floresca (2006) 1 1 1 0 thesis quantitative women 0 post United States Philippines
Ando (2010) 1 1 1 0 thesis quantitative general 0 post United States Japan
Ando (2014) 1 1 1 0 journalArticle quantitative general 0 post United States Japan
Iglesias-Rios et al. (2015) 0 1 0 0 journalArticle quantitative general 0 post United States LatinX
C. Wang (2007) 0 1 1 0 thesis mixed method general 1 post United States China
Khoo (2010) 0 1 0 0 journalArticle quantitative humanitarian 0 post Australia any
Collins (2010) 1 1 1 0 thesis mixed method women 0 post United States Mexico
Ford & Holumyong (2016) 0 1 0 0 journalArticle quantitative women 0 post Thailand any
Ekmanis (2017) 1 1 1 1 thesis mixed method youth 1 post Latvia Russia
Amit (2018) 0 0 1 0 journalArticle quantitative general 0 post Israel former Soviet Union
Rebhun (2014) 0 1 0 0 journalArticle quantitative general 0 post any Israel
Mace et al. (2005) 0 1 1 0 journalArticle mixed method general 0 post Australia any
S. K. Lee & Flores (2019) 1 1 1 0 journalArticle mixed method general 0 post United States any
M. M. Ramos et al. (2011) 0 1 0 0 journalArticle quantitative youth women 0 post United States LatinX
Im et al. (2015) 0 0 1 0 journalArticle quantitative women 1 post United States any
Aretakis (2011) 0 1 1 0 thesis mixed method youth 0 post United States LatinX
Garrido et al. (2012) 0 1 1 0 journalArticle mixed method youth 0 post Spain any
Dalgard & Thapa (2007) 0 1 1 0 journalArticle quantitative general 0 post Norway any
E. Yoon & Lee (2010) 1 1 1 0 journalArticle quantitative general 0 post United States Korea
N. An et al. (2008) 0 1 0 0 journalArticle quantitative general 0 post United States Asia
Schaefer et al. (2009) 0 1 1 0 journalArticle quantitative youth women 0 post United States Mexico, Asia
Doucerain et al. (2017) 1 1 1 0 journalArticle quantitative students 0 post Canada any
Hosung Shin et al. (2005) 0 0 1 0 journalArticle quantitative general 0 post United States Korea
Hall (2018) 0 0 1 0 journalArticle quantitative general 0 post Sweden former Soviet Union
Félix Neto et al. (2019) 0 1 1 0 journalArticle quantitative general 0 post Portugal Ukraine
Gonsoulin & Fu (2010) 0 1 0 0 journalArticle quantitative general 0 post United States LatinX, Asia
Bohra-Mishra & Massey (2015) 0 1 0 0 journalArticle quantitative general 0 post United States any
Ji-yeon Lee & Pistole (2014) 0 1 1 0 journalArticle quantitative students 0 post United States any
Wenli Chen (2010) 1 1 1 0 journalArticle quantitative general 0 post Singapore China
Sabina et al. (2015) 0 1 1 0 journalArticle quantitative women 0 post United States LatinX
Greenman & Xie (2008) 0 1 0 0 journalArticle quantitative youth 0 post United States any
Abu-Rayya & Sam (2017) 1 1 1 0 journalArticle quantitative general 0 post any any
Félix Neto et al. (2019) 0 1 0 0 journalArticle quantitative general 0 post Switzerland Portugal
Schwebel & Brezausek (2009) 0 1 0 0 journalArticle quantitative general 1 post United States any
Adamuti-Trache (2013) 0 1 1 1 journalArticle quantitative general 0 post Canada any
Roth (2018) 1 0 1 0 thesis quantitative general 0 post United States LatinX
Lam & Chan (2004) 1 1 1 1 journalArticle qualitative general 0 post Hong Kong China
Rose M. Perez (2011) 0 0 1 0 journalArticle qualitative youth 0 post United States LatinX
Tegegne (2018) 0 1 0 0 journalArticle quantitative general 0 post United States any
Liu (2007) 1 0 1 0 journalArticle quantitative general 1 post Australia Asia
Sabina et al. (2016) 0 1 1 0 journalArticle quantitative youth 0 post United States LatinX
Meca et al. (2018) 1 1 1 1 journalArticle quantitative youth 0 post United States LatinX
Kane et al. (2016) 0 1 1 0 journalArticle quantitative any 0 post United States Cambodia, Vietnam
Baker (2016) 1 1 1 0 journalArticle quantitative any 0 post United States former Soviet Union
Wals & Rudolph (2019) 0 1 1 0 journalArticle quantitative any 0 post United States LatinX
Vall & Botella (2015) 1 1 1 0 journalArticle mixed method youth 1 post Spain any
Debrosse et al. (2015) 1 1 1 0 journalArticle quantitative youth 0 post Canada any
Reitz et al. (2014) 1 1 1 1 journalArticle quantitative youth 0 post Greece any
Tieu & Konnert (2015) 1 1 1 0 journalArticle quantitative elderly 0 post Canada China
Moon & Park (2007) 1 1 1 0 journalArticle quantitative general 0 post United States Korea
Lindstrom (2019) 0 1 0 0 journalArticle quantitative general 0 post United States Mexico
Updegraff et al. (2012) 1 1 1 0 journalArticle quantitative youth 0 post United States Mexico
Chang & Wallace (2016) 0 1 0 0 journalArticle quantitative spouses 0 post Korea China, Vietnam
Sonderegger & Barrett (2004) 1 0 0 1 journalArticle quantitative youth 0 post Australia former Soviet Union, China
Bowe (2019) 1 1 1 1 journalArticle quantitative youth 0 post Britain any
Birman et al. (2010) 1 1 1 0 journalArticle quantitative youth 0 post United States former Soviet Union
Mannarini et al. (2017) 1 1 1 1 journalArticle quantitative general 0 post Italy Albania, Sri Lanka
Jeong & Kim (2016) 0 0 1 0 journalArticle quantitative refugee women 0 post South Korea North Korea
Irvin et al. (2013) 1 1 1 0 journalArticle quantitative general 0 post United States Korea
Sonderegger & Barrett (2004) 1 0 0 1 journalArticle quantitative youth 0 post Australia former Soviet Union, China
Stevens et al. (2004) 1 1 1 0 journalArticle qualitative general 0 post Netherlands Marocco
Rose M. Perez & Arnold-Berkovits (2019) 1 0 1 1 journalArticle quantitative general 0 post United States LatinX
Daniel et al. (2014) 1 0 1 0 bookSection quantitative youth 0 post Israel, Germany former Soviet Union, Arab, Turkey
Organista & Kubo (2005) 0 1 1 0 journalArticle quantitative day laborers 0 post United States LatinX
Shellman et al. (2014) 1 1 0 0 journalArticle quantitative women 0 post United States LatinX
Grant & Robertson (2014) 1 1 1 0 journalArticle quantitative general 0 post Canada any
Y. Lu et al. (2016) 1 1 1 1 journalArticle quantitative general 0 post Australia China
Felix Neto (2010) 0 0 1 1 journalArticle quantitative youth 0 post Portugal any
Stuart & Ward (2011) 1 1 1 1 journalArticle quantitative youth 0 post New Zealand South Asia
Beiser & Hou (2017) 0 1 1 0 journalArticle quantitative general 0 post Canada any
Diaz (2010) 0 1 1 0 thesis quantitative women 0 post United States Mexico
Bauer et al. (2012) 0 1 0 0 journalArticle quantitative general 1 post United States LatinX, Asia
Shakeel et al. (2018) 0 1 0 0 journalArticle quantitative pregnant women 1 post Norway any
Markides et al. (2012) 0 1 0 0 journalArticle quantitative general 0 post United States Mexico
Briones et al. (2012) 1 1 1 0 journalArticle quantitative youth 0 post Spain Marocco, Ecuador
Shahim (2007) 0 1 1 1 journalArticle quantitative general 0 post Canada Iran
Miyoshi et al. (2017) 1 1 1 0 journalArticle quantitative sojourner 0 post United States Japan
Lim et al. (2011) 1 1 1 1 journalArticle quantitative youth 0 post United States Vietnam, Cambodia
Sharapova & Goguikian Ratcliff (2018) 1 1 1 0 journalArticle quantitative women 0 post Switzerland any
Saint-Jean et al. (2008) 0 1 0 0 journalArticle quantitative youth 1 post United States any
Newman & Sahak (2012) 0 0 1 0 journalArticle quantitative general 0 post United Kingdom Malaysia
W. Y. Chan & Latzman (2015) 1 0 1 0 journalArticle quantitative youth 0 post United States any
Delforterie et al. (2014) 1 1 1 0 journalArticle quantitative youth 0 post Netherlands any
Potocky-Tripodi (2003) 0 0 1 1 journalArticle quantitative refugee 0 post United States Hmong, Somalia, Russia
Kanaparthi (2009) 0 1 1 0 thesis qualitative students 0 post United States any
Saedi (2009) 1 1 1 0 thesis quantitative Jewish 0 post United States Iran
Musso et al. (2017) 1 1 1 0 journalArticle quantitative youth 0 post Italy Tunesia
Friedman & Saroglou (2010) 1 1 1 0 journalArticle quantitative muslim 0 post Belgium any
Zan & Fan (2018) 0 1 0 0 journalArticle quantitative general 0 post United States any
Al‐Rajhi et al. (2013) 0 1 1 0 journalArticle quantitative general 0 post Saudi Arabia any
Cook et al. (2009) 1 1 1 0 journalArticle quantitative youth 0 post United States Korea
Jongwon Lee (2007) 1 1 1 0 thesis quantitative women 0 post United States Korea
Joseph et al. (2013) 1 1 1 1 journalArticle quantitative general 0 post United States Caribbeans
S. X. Chen et al. (2013) 0 1 0 0 journalArticle quantitative non-married 0 post Taiwan Asia
Guerra et al. (2019) 0 0 1 0 journalArticle quantitative youth 1 post Portugal any
R. Fitzgerald et al. (2014) 0 0 1 0 journalArticle quantitative general 1 post Europe any
Akresh et al. (2016) 0 1 1 0 journalArticle quantitative general 0 post United States LatinX
Decker et al. (2007) 0 1 0 0 journalArticle quantitative youth women 1 post United States any
Reiss et al. (2015) 1 1 1 1 journalArticle quantitative women 0 post Germany Turkey
Jurcik et al. (2014) 1 1 1 0 bookSection quantitative general 0 post Canada any, former Soviet Union
Amit (2012) 0 0 1 0 journalArticle quantitative general 0 post Israel any
Maliepaard & Phalet (2012) 0 1 0 0 journalArticle quantitative general 0 post Netherlands Marocco, Turkey
Abraído-Lanza et al. (2017) 0 1 1 0 journalArticle quantitative women 0 post United States LatinX
Fuligni et al. (2008) 0 0 1 0 journalArticle quantitative youth 0 post United States LatinX, Asia
H. M. Ngo & Le (2007) 0 1 0 0 journalArticle quantitative youth 0 post United States Cambodia, China, Laos, Vietnam
E. J. Lee et al. (2017) 0 1 0 0 journalArticle quantitative worker 0 pre & post Korea Southeast Asia
Paparusso (2019) 1 0 1 0 journalArticle quantitative general 0 post Europe any
Morela et al. (2013) 1 1 1 1 journalArticle quantitative athletes youth 0 post Greece any
C. Leung & Karnilowicz (2009) 1 1 1 1 journalArticle quantitative youth 1 post Australia, Hong Kong China
Commodore-Mensah et al. (2016) 1 0 1 0 journalArticle quantitative general 0 post United States Ghana, Nigeria
Shi et al. (2012) 0 1 0 0 journalArticle quantitative youth 0 post United States LatinX
Kimber et al. (2015) 0 1 1 1 journalArticle quantitative general 1 post United States any
Nakash et al. (2016) 1 0 0 1 journalArticle quantitative refugee women 0 post Israel Eritrea
Zalishahar (2020) 0 1 1 1 thesis quantitative general 1 post United States Iran
Ward et al. (2011) 1 0 1 0 journalArticle quantitative general 1 post New Zealand any
Mchitarjan & Reisenzein (2015) 1 1 1 1 journalArticle quantitative general 0 post any any
Souiden & Ladhari (2011) 1 1 1 0 journalArticle quantitative general 0 post Canada West Africa
Cuevas et al. (2012) 0 1 1 0 journalArticle quantitative women 0 post United States LatinX
Sabina et al. (2013) 0 1 1 0 journalArticle quantitative women 0 post United States LatinX
Wassermann et al. (2017) 1 0 1 0 journalArticle quantitative students 0 post United States LatinX
Plooy et al. (2019) 1 0 1 0 journalArticle qualitative general 1 post Australia any
Martin et al. (2007) 0 1 0 0 journalArticle quantitative parent 1 post United States Mexico
Christ et al. (2013) 0 0 1 0 journalArticle quantitative general 1 post Germany any
J. Choi et al. (2014) 1 1 1 1 journalArticle qualitative women 0 post United States Korea
Van Hook et al. (2015) 0 1 0 0 journalArticle quantitative general 1 post United States any
Bhaloo (2011) 1 1 1 0 thesis quantitative general 0 post United States India
Vasquez Guzman & Sanchez (2019) 0 1 0 0 journalArticle quantitative general 0 post United States LatinX
Jang et al. (2014) 0 1 1 0 journalArticle quantitative elderly 0 post United States Korea
Steele et al. (2020) 0 1 0 0 journalArticle quantitative general 1 post United States any
Lopez-Zafra & El Ghoudani (2014) 0 1 1 0 journalArticle quantitative women 0 post Spain Marocco
Sussner et al. (2008) 0 1 1 0 journalArticle mixed method general 0 post United States LatinX
Hoggatt et al. (2012) 0 1 1 0 journalArticle quantitative mothers 1 post United States LatinX
H. M. González et al. (2011) 1 1 1 0 journalArticle quantitative elderly 0 post United States LatinX
Lönnqvist et al. (2015) 0 1 1 0 journalArticle quantitative general 0 pre & post Finnland Russia
Dali (2010) 1 1 1 1 thesis qualitative general 0 post Canada former Soviet Union
Bryant & Kim (2013) 0 1 1 0 journalArticle quantitative elderly 1 post United States LatinX, Asia
Gonidakis et al. (2011) 0 1 1 0 journalArticle quantitative general 0 post Greece any
Ince et al. (2014) 1 1 1 1 journalArticle quantitative general 0 post Netherlands Turkey
Venkatesh et al. (2017) 1 1 1 0 journalArticle quantitative general 0 post United States Asian Indian
Prapas & Mavreas (2019) 1 1 1 0 journalArticle quantitative general 1 post Greece Albania, Pontic Greeks
E. V. Kateri et al. (2019) 0 0 1 0 journalArticle quantitative general 0 post Crete, Greece India
Jia et al. (2014) 1 1 1 0 journalArticle quantitative youth 0 post Canada China
Orjiako & So (2014) 0 1 0 0 journalArticle quantitative general 0 post United States sub-Saharan Africa, Ethopia, Nigeria
Xie & Greenman (2011) 0 1 0 0 journalArticle quantitative youth 0 post United States any
W. Kim & Chen (2011) 0 1 0 0 journalArticle quantitative elderly 0 post Canada Korea
Amit & Litwin (2010) 1 1 1 0 journalArticle quantitative elderly 0 post Israel any
Titzmann & Jugert (2017) 0 1 0 0 journalArticle quantitative women 0 post Germany any
Schubert et al. (2019) 0 1 0 0 journalArticle quantitative general 0 post Finnland Russia, Somalia, Kurdistan
Kartal et al. (2018) 0 1 1 0 journalArticle quantitative general 0 post Australia, Austria Bosnia
Jasini et al. (2019) 1 0 0 0 journalArticle quantitative youth 1 post Belgium any
Özbek et al. (2015) 1 0 1 0 journalArticle quantitative youth 0 post Netherlands Morocco
Kershaw et al. (2012) 0 1 0 0 journalArticle quantitative general 1 post United States Mexico
Nagaraj et al. (2018) 1 0 1 0 journalArticle quantitative women 0 post United States South Asia
Feliciano (2011) 1 1 1 0 thesis qualitative women 0 post Australia Hmong
B.-H. Kim (2013) 0 1 1 0 thesis mixed method general 0 post Canada Korea
Jurcik et al. (2015) 1 1 1 0 journalArticle quantitative general 0 post Canada former Soviet Union
Dillon et al. (2009) 0 1 1 0 journalArticle quantitative women 0 post United States LatinX
Jadalla & Lee (2015) 1 1 1 0 journalArticle quantitative general 0 post United States Arab
Yijälä et al. (2012) 0 0 1 0 journalArticle quantitative general 0 potential Finland Russia
Mancini et al. (2018) 0 1 1 1 journalArticle quantitative youth 1 post Spain, Italy any
Okamoto et al. (2020) 0 1 1 0 journalArticle mixed method general 0 post United States Mexico, India
M. R. Ramos et al. (2015) 1 1 0 1 journalArticle quantitative general 0 post United Kingdom Poland
Ryu et al. (2013) 0 1 0 0 journalArticle quantitative elderly women 0 post United States Asia
Buckingham (2017) 0 1 1 1 thesis mixed method general 0 post United States Mexico, spanish-speaking countries of Central or South America
Jang et al. (2019) 0 0 1 0 journalArticle quantitative general 0 post United States Asia
Gunasekara et al. (2019) 1 1 1 0 journalArticle quantitative skilled migrants 0 post Australia India, Sri Lanka

Theoretical Literature

Descriptives

Theory type

The authors of the 93 included theoretical works self-categorized their contributions as a theoretical conceptualization (N = 9), theoretical framework (N = 26), theory (N = 36), or theoretical model (N = 22; also see Table 5).

data.frame(table(Type = dt.Theories.Included$FrameworkTheoryModel)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Type of Theoretical Work",
      format = "html",
      col.names = c("Type",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 5: Type of Theoretical Work
Type Frequency
Theory 36
Framework 26
Model 22
Conceptualization 9

Focus

And while 29 authors explicitly targeted a specific part of acculturation (e.g., 7 identity acculturation theories and 4 labor market acculturation theories; also see Table 7), a majority of theoretical works offered commentary on the overall construct of acculturation (N = 64; also see Table 6).

data.frame(table(Focus = dt.Theories.Included$GeneralAspect)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Focus of Theoretical Work",
      format = "html",
      col.names = c("Focus",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 6: Focus of Theoretical Work
Focus Frequency
General 64
Aspect 29 
data.frame(table(Aspect = dt.Theories.Included$Target)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Aspect Frequency in Specific Theoretical Works",
      format = "html",
      col.names = c("Aspect",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 7: Aspect Frequency in Specific Theoretical Works
Aspect Frequency
Identity 7
Work 4
Consumption 3
Health 3
Politics 2
Ambiguity 1
Education 1
Family 1
Language 1
Loss of Homeland 1
Media 1
Money 1
Motive 1
Remote 1
Well-Being 1

Source

Looking at the types of theory building, a majority of proposal were purely theoretical (N = 76) with the remaining theoretical works growing out of qualitative investigations (such as grounded theory approaches; N = 17; also see Table 8).

data.frame(table(Focus = dt.Theories.Included$GeneralAspect)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Source of Theoretical Work",
      format = "html",
      col.names = c("Source",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 8: Source of Theoretical Work
Source Frequency
General 64
Aspect 29

Time

We also assessed when theoretical works were published to gain a feel for a theoretical interest in the topic.

# marginal histogram of number of manuscripts per year (with minimal theme)
theoryYearHist <- dt.Theories.Included %>%
  dplyr::select(year) %>%
  mutate(year = as.POSIXct(year, format = "%Y")) %>%
  ggplot(., aes(x=year)) + 
  geom_histogram(bins = length(table(dt.Theories.Included$year)), fill = "grey14")+
  labs(title = "Year of Publication [Methodological works]",
       y = "Number of Validations",
       x = "Year") +
  scale_y_continuous(breaks = seq(0,20,10)) +
  scale_x_datetime(breaks = as.POSIXct(as.character(seq(1920,2020,10)), format = "%Y"), date_labels = "%Y") +
  theme_Publication() +
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_blank(), #element_text(size=16, face="bold", hjust = 0.5),
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        axis.ticks.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.x = element_blank(),
        axis.line.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        plot.margin = unit(c(5, 5, 0, 5), "pt"),
        legend.position="none")

# cumulative sum of articles over time
theoryYearCum <- data.frame(table(Year = dt.Theories.Included$year)) %>%
  mutate(Year = as.POSIXct(Year, format = "%Y"),
         CumSum = cumsum(Freq)) %>%
  ggplot(., aes(x=Year, y=CumSum, group=1)) +
  geom_point() +
  geom_line() +
  scale_y_continuous(breaks = seq(0,90,10)) +
  scale_x_datetime(breaks = as.POSIXct(as.character(seq(1920,2020,10)), format = "%Y"), date_labels = "%Y") +
  labs(title = "Year of Publication [Theoretical works]",
       y = "Cumulative Sum",
       x = "Year") +
  theme_Publication() +
  theme(strip.background = element_rect(fill="grey14", color="grey14", ),
        axis.title.x = element_text(size=14, face="bold"),
        axis.title.y = element_text(size=14, face="bold"),
        plot.title = element_blank(), #element_text(size=16, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=14),
        axis.text.y = element_text(size=14),
        #panel.grid.major.x = element_blank(),
        #panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        plot.margin = unit(c(0, 5, 5, 5), "pt"),
        legend.position="none")

# combine graphs
cowplot::plot_grid(theoryYearHist, theoryYearCum, nrow=2, align = "v", rel_heights = c(1/5, 4/5))
Year of Publication [Theoretical works]. The graph shows the cumulative sum in the main graph as well as the number of works published over time in the marginal graph.

Figure 2: Year of Publication [Theoretical works]. The graph shows the cumulative sum in the main graph as well as the number of works published over time in the marginal graph. 

# potentially type of theoretical works over time.

Experience

names <- c("Affect", "Behavior", "Cognition", "Desire")
kTheo <- dt.Theories.Included %>%
  select(Affect, Affect2, Behavior, Behavior2, Cognition, Cognition2, Desire, Desire2) %>%
  mutate_all(~replace_na(., 0)) %>%
  kappa.full.multiple(., names)
kTheoPooled <- kappa.pooled2(kTheo)

We then look at the use of experience aspects within the theoretical works in more detail. Before we inspect the aspect inclusion patterns in more detail we assessed the inter-rater reliability. Because the affect, behavior, cognition, and desire codings were integral to the framework two independent raters coded all included manuscripts. All four experience aspects were coded as either being included [1] or not included [0]. We, thus, used Cohen’s \(\kappa\) to assess the chance-corrected agreement between the raters. Confidence intervals were calculated using the standard error formula provided in McHugh (2012). Pooled Cohen’s \(\kappa\)s were calculated using the methods developed by De Vries et al. (2008). Note that for the pooled \(\kappa\) we provide basic bootstrapped confidence intervals, which should be treated as preliminary because thus far no validated calculation for associated standard errors have been established (to the best of our knowledge). All inter-rater agreements were 96.77% or above and all \(\kappa\)s were above 0.82 (\(\kappa_{pooled}\) = 0.94, \(95\%CI_{boot}\)[0.91, 1.00]; full inter-rater reliability is available in Table 9).

kTheo %>%
  kbl(.,
        #label = "",
        caption = "Theoretical Literature: <br>Cohen's $\\kappa$",
        format = "html",
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(.)-1)))  %>%
  footnote(general = paste0("$\\kappa_{pooled}$ = ",format(round(kTheoPooled$k.pooled, 2), nsmall=2), ", $95\\%CI_{boot}$(", format(round(kTheoPooled$lwr, 2), nsmall=2), ", ", format(round(kTheoPooled$upr, 2), nsmall=2), ")")) %>%
  kable_classic(full_width = F,
                lightable_options = "hover",
                html_font = "Cambria")
Table 9: Theoretical Literature:
Cohen’s \(\kappa\)
name Po Pe k se lwr upr
Affect 0.9892 0.5036 0.9783 0.0215 0.9361 1.021
Behavior 0.9785 0.8793 0.8218 0.1246 0.5776 1.066
Cognition 0.9892 0.8165 0.9414 0.0583 0.8272 1.056
Desire 0.9677 0.5147 0.9335 0.0378 0.8595 1.008
Note:
\(\kappa_{pooled}\) = 0.94, \(95\%CI_{boot}\)(0.91, 1.00)

We were particularly interested in the overall use of each experience aspect (see Table 10 and Figure 3), the combined uses of experience aspects (see Table 11 and Figure 4), the resulting distribution of the number of aspects considered (see Table 12 and Figure 5), as well as the number of other aspects that were considered with each of the aspects (see Table 13).

# Count the times each dimension is measured
TheoElementFreq <- dt.Theories.Included %>%
  dplyr::select(Affect = AffectFinal, Behavior = BehaviorFinal, Cognition = CognitionFinal, Desire = DesireFinal) %>%
  mutate_at(vars(Affect, Behavior, Cognition, Desire), ~replace_na(., 0)) %>%
  colSums(., na.rm = FALSE, dims = 1) 

# transform to data frame and make row names name variable
TheoElementFreq <- data.frame(Element = names(TheoElementFreq), 
                            Frequency = TheoElementFreq, 
                            Percentage = TheoElementFreq/nrow(dt.Theories.Included)*100) %>%
  mutate(Element = fct_reorder(Element, Frequency))

TheoElementFreq %>%
  kbl(., 
        #label = "",
        caption = "Theoretical Literature: <br>Overall Aspect Frequency",
        format = "html", 
        #linesep = "",
        #booktabs = T,
        align = c('l', 'c', 'c'))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 10: Theoretical Literature:
Overall Aspect Frequency
Element Frequency Percentage
Affect Affect 44 47.31
Behavior Behavior 88 94.62
Cognition Cognition 84 90.32
Desire Desire 39 41.94 
# barplot of dimension frequency
TheoABCDBar <- ggplot(data=TheoElementFreq, aes(x=Element, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    #aes(label = paste0("N = ",Frequency)),
    aes(label = paste0(format(round(Percentage,2), nsmall=2), "%")),
    position=position_stack(vjust=0.5),
    color = "white",
    size = 4,
    vjust = 0.5
    ) +
  labs(#title = "Aspect Frequency",
       y = "Percentage across all Theories",
       x = "Experience Aspect")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size=10, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=10),
        axis.text.y = element_text(size=10),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        legend.position="none")

TheoABCDBar
Theoretical Literature: Bar Graph Aspect Frequency

Figure 3: Theoretical Literature: Bar Graph Aspect Frequency 

# frequency of unique combinations
TheoElementCombFreq <- dt.Theories.Included %>%
  dplyr::select(Affect = AffectFinal, Behavior = BehaviorFinal, Cognition = CognitionFinal, Desire = DesireFinal) %>%
  #mutate_at(vars(Affect, Behavior, Cognition, Desire), ~replace_na(., 0)) %>%
  group_by(Affect, Behavior, Cognition, Desire) %>%
  summarise(Frequency = n()) %>%
  ungroup() %>%
  mutate(complexity = rowSums(dplyr::select(., Affect, Behavior, Cognition, Desire), na.rm = T))

# fill replace ones with colnames to be combined
for (i in 1:4) {
    TheoElementCombFreq[[i]] <- str_replace(as.character(TheoElementCombFreq[[i]]), "1", colnames(TheoElementCombFreq)[i])
}

# collect Elements names for each combination
TheoElementCombFreq <- TheoElementCombFreq %>%
  unite("ExperienceCombination",c("Affect", "Behavior", "Cognition", "Desire"), na.rm = TRUE, sep = ", ") %>%
  mutate(ExperienceCombination = fct_reorder(ExperienceCombination, Frequency),
         Percentage = Frequency/nrow(dt.Theories.Included)*100,
         Affect = ifelse(grepl("Affect", ExperienceCombination, fixed = TRUE), 1,0),
         Behavior = ifelse(grepl("Behavior", ExperienceCombination, fixed = TRUE), 1,0),
         Cognition = ifelse(grepl("Cognition", ExperienceCombination, fixed = TRUE), 1,0),
         Desire = ifelse(grepl("Desire", ExperienceCombination, fixed = TRUE), 1,0)) %>%
  arrange(-Frequency)

TheoElementCombFreq %>%
  kbl(., 
        #label = "",
        caption = "Theoretical Literature: Aspect Combinations",
        format = "html", 
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(TheoElementCombFreq)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 11: Theoretical Literature: Aspect Combinations
ExperienceCombination Frequency complexity Percentage Affect Behavior Cognition Desire
Behavior, Cognition 26 2 27.957 0 1 1 0
Affect, Behavior, Cognition, Desire 22 4 23.656 1 1 1 1
Affect, Behavior, Cognition 16 3 17.204 1 1 1 0
Behavior, Cognition, Desire 15 3 16.129 0 1 1 1
Behavior 5 1 5.376 0 1 0 0
Affect, Behavior 3 2 3.226 1 1 0 0
Affect, Cognition 3 2 3.226 1 0 1 0
Behavior, Desire 1 2 1.075 0 1 0 1
Cognition, Desire 1 2 1.075 0 0 1 1
Cognition 1 1 1.075 0 0 1 0 
# bar plot frequencies
TheoABCDComb <- ggplot(TheoElementCombFreq, aes(x=ExperienceCombination, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    aes(y=Percentage, label = paste0(format(round(Percentage,2), nsmall=2),"% [N = ", Frequency, "]")),
    color = "grey14",
    size = 4,
    hjust = -.1,
    inherit.aes = TRUE
    ) +
  scale_y_continuous(limits = c(0, ceiling(max(TheoElementCombFreq$Percentage)*1.15)),
                     breaks = seq(0, ceiling(max(TheoElementCombFreq$Percentage)*1.15), 5))+
  labs(y = "Proportion of all theories [in %]",
       x = "Combination of Experience Aspects")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")

TheoABCDComb
Theoretical Literature: Bar Graph Aspect Combinations

Figure 4: Theoretical Literature: Bar Graph Aspect Combinations 

# summarize by aspect complexity
TheoComplexity <- TheoElementCombFreq %>%
  dplyr::select(complexity, Frequency) %>%
  group_by(complexity) %>%
  summarise(Frequency = sum(Frequency),
            Percentage = sum(Frequency)/nrow(dt.Theories.Included)*100) %>%
  ungroup() %>%
  mutate(complexity = as.factor(complexity),
         complexity = fct_reorder(complexity, Frequency))

# overall complexity mean and standard deviation
TheoComplexityAverage <- weighted.mean(as.numeric(as.character(TheoComplexity$complexity)), TheoComplexity$Frequency)
TheoComplexitySD <- wtd.var(x = as.numeric(as.character(TheoComplexity$complexity)), weights = TheoComplexity$Frequency)

# Table complexity distribution
TheoComplexity %>%
  kbl(., 
        #label = "",
        caption = "Theoretical Literature: <br>Number of Aspects considered",
        format = "html", 
        digits = 2,
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(TheoComplexity)-1)))  %>%
  footnote(general = paste0("M = ",format(round(TheoComplexityAverage, 2), nsmall=2), ", SD = ", format(round(TheoComplexitySD, 2), nsmall=2))) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 12: Theoretical Literature:
Number of Aspects considered
complexity Frequency Percentage
1 6 6.45
2 34 36.56
3 31 33.33
4 22 23.66
Note:
M = 2.74, SD = 0.80 
# barplot of complexity frequency
TheoComplexityBar <- ggplot(data=TheoComplexity, aes(x=complexity, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    #aes(label = paste0("N = ",Frequency)),
    aes(label = paste0(format(round(Percentage,2), nsmall=2), "%")),
    position=position_stack(vjust=0.5),
    color = "white",
    size = 4,
    vjust = 0.5
    ) +
  labs(title = "Numer of Aspects Considered",
       y = "Frequency across all Theories",
       x = "Number of Aspects Considered")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14", ),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size=10, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=10),
        axis.text.y = element_text(size=10),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        legend.position="none")

TheoComplexityBar
Theoretical Literature: Bar Graph Number of Aspects considered

Figure 5: Theoretical Literature: Bar Graph Number of Aspects considered 

# Numer of Aspects Considered for each element
TheoElementComplexity <- TheoElementCombFreq %>%
  gather(key = "Element", value = "ElementDum", Affect, Behavior, Cognition, Desire) %>%
  filter(ElementDum == 1) %>%
  group_by(Element) %>%
  summarise(n = sum(Frequency),
            avgComplexity = weighted.mean(x = complexity, w = Frequency),
            sdComplexity = wtd.var(x = complexity, weights = Frequency)) %>%
  ungroup() %>%
  mutate(seComplexity = sdComplexity/sqrt(n)) %>%
  arrange(-avgComplexity)

# Table Aspect complexity distribution
TheoElementComplexity %>%
  kbl(., 
        #label = "",
        caption = "Theoretical Literature: <br>Number of Aspects considered with each aspect",
        col.names = c("N", "Aspect", "Mean", "Standard Deviation", "Standard Error"),
        format = "html", 
        digits = 2,
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(TheoElementComplexity)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 13: Theoretical Literature:
Number of Aspects considered with each aspect
N Aspect Mean Standard Deviation Standard Error
Desire 39 3.51 0.36 0.06
Affect 44 3.36 0.52 0.08
Cognition 84 2.88 0.66 0.07
Behavior 88 2.80 0.79 0.08

We additionally inspected bi-variate relations between the individual aspects. We calculate the phi coefficient (for binary variables) together with the raw number of co-occurrences (see Table 14).

# make crossproduct matrix to condense co-occurrences (off-diagonals) and get frequencies (diagonals)
as.matrix(dt.Theories.Included %>% dplyr::select(Affect = AffectFinal, Behavior = BehaviorFinal,    Cognition = CognitionFinal, Desire = DesireFinal) %>%
            mutate_all(~replace(., is.na(.), 0))) %>%
  BinaryCor(., "pearson") %>%
  tibble::rownames_to_column(., var = "Aspect") %>%
  kbl(., 
        #label = "",
        caption = "Theoretical Literature: <br>Aspects Bi-Variate Relations",
        format = "html", 
        linesep = "",
        booktabs = T,
        align = c('l', rep('c', ncol(.)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 14: Theoretical Literature:
Aspects Bi-Variate Relations
Aspect Affect Behavior Cognition Desire
Affect N = 44 -0.06 0.09 0.15
Behavior 41 N = 88 -0.08 0.11
Cognition 41 79 N = 84 0.20*
Desire 22 38 38 N = 39 
# draw combined graph
ggdraw() +
  draw_plot(TheoABCDComb, x = 0, y = 0.3, width = 1, height = .7)+
  draw_plot(TheoABCDBar, x = 0, y = 0, width = .55, height = .3) +
  draw_plot(TheoComplexityBar, x = .55, y = 0, width = .45, height = .3) +
  draw_plot_label(c("(A)", "(B)", "(C)"), c(0, 0, 0.55), c(1, 0.3, 0.3), size = 15)
Theoretical Literature: Combined Bar Graphs

Figure 6: Theoretical Literature: Combined Bar Graphs

Process

To assess the focus on psychological acculturation as a process or an outcome, we coded whether authors self-identified the theory as a process (e.g., ‘process’, ‘development’, ‘longitudinal’, ‘temporal’, ‘dynamic’) or an outcome (e.g., ‘static’, ‘outcome’, ‘markers’, ‘consequence’).

dt.Theories.Included %>%
  dplyr::select(Time) %>%
  mutate(Time = replace_na(Time, "N/A")) %>%
  group_by(Time) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Theoretical Literature: <br>Process Focus",
      format = "html",
      digits = 2,
      col.names = c("Conceptualisation", "Frequency", "Percentage")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 15: Theoretical Literature:
Process Focus
Conceptualisation Frequency Percentage
dynamic 49 52.69
static 44 47.31

We find that a slight majority of theories focuses on dynamic conceptualizations of psychological acculturation. This is a relatively high percentage, considering that past reviews of the acculturation literature have pointed to a small number of studies actually testing dynamic theories (Ward & Szabó, 2019).

Methodological Literature

Descriptives

Scale Characteristics

To gain a broad understanding of the methodological diversity in scale constructions we assessed the distributions of the ‘number of items measured’, ‘number of subscales’, and ‘number of response options’ (see Table 16).

# Count whether validations included Majority and make Table
scaleDescrCont <- dt.Scales.Included %>%
  dplyr::select(NItems, NSubScales, ResponseRange) %>%
  mutate_all(as.numeric)

scaleDescrCont %>%
  psych::describe(., trim = .2) %>%
  as.data.frame %>%
  mutate(vars = rownames(.),
         na = nrow(dt.Scales.Included)-n,
         win.mean = sapply(scaleDescrCont,psych::winsor.mean,simplify=T),
         win.sd = sapply(scaleDescrCont,psych::winsor.sd,simplify=T)) %>%
  dplyr::select(characteristic = vars, n, na, 
                mean, `mean win` = win.mean, `mean trim` = trimmed, median,
                sd, `sd win` = win.sd, MAD = mad, min, max,
                skew, kurtosis) %>%
  kbl(., 
      #label = "",
      caption = "Methodological Literature: Continuous Scale Characteristics",
      format = "html", 
      #linesep = "",
      #booktabs = T,
      row.names = F,
      digits = 2,
      align = c('l', rep('c', ncol(.)-1)))  %>%
  add_header_above(., c(" " = 3,"Centrality" = 4, "Dispersion" = 5, "Distribution" = 2)) %>%
  footnote(general = "'na' indicates the number of scales that were excluded because they had unknown or multiple characteristics.") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 16: Methodological Literature: Continuous Scale Characteristics
Centrality
Dispersion
Distribution
characteristic n na mean mean win mean trim median sd sd win MAD min max skew kurtosis
NItems 231 2 21.20 18.00 17.35 16 18.06 8.70 11.86 1 136 2.60 11.35
NSubScales 220 13 2.82 2.44 2.39 2 2.06 1.23 1.48 0 13 1.64 3.82
ResponseRange 195 38 5.17 5.00 5.00 5 1.39 0.68 0.00 2 10 0.77 1.87
Note:
‘na’ indicates the number of scales that were excluded because they had unknown or multiple characteristics.

Sample

We firstly assessed how many of the scale validations included the dominant group was measured as well (see Table 17).

# Count whether validations included Majority
scalesMajFreq <- dt.Scales.Included %>%
  dplyr::select(IncludesMajority) %>%
  filter(!is.na(IncludesMajority)) %>%
  mutate(`Includes Majority` = recode_factor(.$IncludesMajority, `0` = "no", `1` = "yes")) %>%
  group_by(`Includes Majority`) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100)

scalesMajFreq %>%
  kbl(., 
      #label = "",
      caption = "Methodological Literature: <br>Dominant Group Included Frequency",
      format = "html", 
      #linesep = "",
      #booktabs = T,
      digits = 2,
      align = c('l', rep('c', ncol(.)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 17: Methodological Literature:
Dominant Group Included Frequency
Includes Majority Frequency Percentage
no 163 71.81
yes 62 27.31
? 1 0.44
N/A 1 0.44

Of the scale we included we also plotted the publication years of the scale validations in order to gain an understanding of the interest in scale development over time (see Figure 7).

# marginal histogram of number of manuscripts per year (with minimal theme)
scaleHist <- dt.Scales.Included %>%
  dplyr::select(year) %>%
  mutate(year = as.POSIXct(year, format = "%Y")) %>%
  ggplot(., aes(x=year)) + 
  geom_histogram(bins = length(table(dt.Scales.Included$year)), fill = "grey14") +
  labs(title = "Year of Publication [Methodological works]",
       y = "Number of Validations",
       x = "Year") +
  #scale_y_continuous(breaks = seq(0,20,10)) +
  scale_x_datetime(breaks = as.POSIXct(as.character(seq(1920,2020,10)), format = "%Y"), date_labels = "%Y") +
  theme_Publication() +
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_blank(), #element_text(size=16, face="bold", hjust = 0.5),
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        axis.ticks.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.x = element_blank(),
        axis.line.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        plot.margin = unit(c(5, 5, 0, 5), "pt"),
        legend.position="none")

# Cumulative Sum of articles published over time
scalesYearCum <- data.frame(table(Year = dt.Scales.Included$year)) %>%
  mutate(Year = as.POSIXct(Year, format = "%Y"),
         CumSum = cumsum(Freq)) %>%
  ggplot(., aes(x=Year, y=CumSum, group=1)) +
  geom_point() +
  geom_line() +
  scale_y_continuous(breaks = seq(0,220,20)) +
  scale_x_datetime(breaks = as.POSIXct(as.character(seq(1920,2020,10)), format = "%Y"), date_labels = "%Y") +
  labs(title = "Year of Publication [Methodological works]",
       y = "Cumulative Sum",
       x = "Year") +
  theme_Publication() +
  theme(strip.background = element_rect(fill="grey14", color="grey14", ),
        axis.title.x = element_text(size=14, face="bold"),
        axis.title.y = element_text(size=14, face="bold"),
        plot.title = element_blank(), #element_text(size=16, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=14),
        axis.text.y = element_text(size=14),
        #panel.grid.major.x = element_blank(),
        #panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        plot.margin = unit(c(0, 5, 5, 5), "pt"),
        legend.position="none")

# combine the two plots
cowplot::plot_grid(scaleHist, scalesYearCum, nrow=2, align = "v", rel_heights = c(1/5, 4/5))
Year of Publication [Methodological works]. The graph shows the cumulative sum in the main graph as well as the number of works published over time in the marginal graph.

Figure 7: Year of Publication [Methodological works]. The graph shows the cumulative sum in the main graph as well as the number of works published over time in the marginal graph.

Experience

names <- c("Affect", "Behavior", "Cognition", "Desire")
kMeth <- dt.Scales.Included %>%
  select(Affect, Affect2, Behavior, Behavior2, Cognition, Cognition2, Desire, Desire2) %>%
  mutate_all(~replace_na(., 0)) %>%
  kappa.full.multiple(., names)
kMethPooled <- kappa.pooled2(kMeth)

We then look at the use of experience aspects within the theoretical works in more detail. All inter-rater agreements were 97.85% or above and all \(\kappa\)s were above 0.95 (\(\kappa_{pooled}\) = 0.96, \(95\%CI_{boot}\)[0.95, 0.98]; for full inter-rater reliability see Table 18).

kMeth %>%
  kbl(., 
      #label = "",
      caption = "Methodological Literature: <br>Cohen's $\\kappa$",
      format = "html", 
      #linesep = "",
      #booktabs = T,
      align = c('l', rep('c', length(.)-1)),
      digits=3)  %>%
  footnote(general = paste0("$\\kappa_{pooled}$ = ",format(round(kMethPooled$k.pooled, 2), nsmall=2), ", $95\\%CI_{boot}$[", format(round(kMethPooled$lwr, 2), nsmall=2), ", ", format(round(kMethPooled$upr, 2), nsmall=2), "]")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 18: Methodological Literature:
Cohen’s \(\kappa\)
name Po Pe k se lwr upr
Affect 0.991 0.500 0.983 0.012 0.959 1.007
Behavior 0.979 0.600 0.946 0.024 0.900 0.993
Cognition 0.991 0.782 0.961 0.028 0.906 1.015
Desire 0.983 0.590 0.958 0.021 0.917 0.999
Note:
\(\kappa_{pooled}\) = 0.96, \(95\%CI_{boot}\)[0.95, 0.98]

We were particularly interested in the overall use of each experience aspect (see Table 19 and Figure 3), the combined uses of experience aspects (see Table 20 and Figure 9), the resulting distribution of the number of aspects considered (see Table 21 and Figure 10), as well as the number of other aspects that were considered with each of the aspects (see Table 22).

# Count the times each dimension is measured
scalesElementFreq <- dt.Scales.Included %>%
  dplyr::select(Affect = AffectFinal, Behavior = BehaviorFinal, Cognition = CognitionFinal, Desire = DesireFinal) %>%
  mutate_at(vars(Affect, Behavior, Cognition, Desire), ~replace_na(., 0)) %>%
  colSums(., na.rm = FALSE, dims = 1) 

# transform to data frame and make row names name variable
scalesElementFreq <- data.frame(Element = names(scalesElementFreq), 
                            Frequency = scalesElementFreq, 
                            Percentage = scalesElementFreq/nrow(dt.Scales.Included)*100) %>%
  mutate(Element = fct_reorder(Element, Frequency)) %>%
  arrange(desc(Frequency))

scalesElementFreq %>%
  kbl(., 
        #label = "",
        caption = "Methodological Literature: <br>Overall Aspect Frequency",
        format = "html", 
        #linesep = "",
        #booktabs = T,
        align = c('l', 'c', 'c'))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 19: Methodological Literature:
Overall Aspect Frequency
Element Frequency Percentage
Cognition Cognition 204 87.55
Behavior Behavior 169 72.53
Affect Affect 117 50.21
Desire Desire 68 29.18 
# barplot of dimension frequency
scalesABCDBar <- ggplot(data=scalesElementFreq, aes(x=Element, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    #aes(label = paste0("N = ",Frequency)),
    aes(label = paste0(format(round(Percentage,2), nsmall=2), "%")),
    position=position_stack(vjust=0.5),
    color = "white",
    size = 4,
    vjust = 0.5
    ) +
  labs(#title = "Aspect Frequency",
       y = "Percentage across all Scales",
       x = "Experience Aspect")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size=10, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=10),
        axis.text.y = element_text(size=10),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        legend.position="none")

scalesABCDBar
Methodological Literature: Bar Graph Aspect Frequency

Figure 8: Methodological Literature: Bar Graph Aspect Frequency 

# frequency of unique combinations
scalesElementCombFreq <- dt.Scales.Included %>%
  dplyr::select(Affect = AffectFinal, Behavior = BehaviorFinal, Cognition = CognitionFinal, Desire = DesireFinal) %>%
  #mutate_at(vars(Affect, Behavior, Cognition, Desire), ~replace_na(., 0)) %>%
  group_by(Affect, Behavior, Cognition, Desire) %>%
  summarise(Frequency = n()) %>%
  ungroup() %>%
  mutate(complexity = rowSums(dplyr::select(., Affect, Behavior, Cognition, Desire), na.rm = T))

# fill replace ones with colnames to be combined
for (i in 1:4) {
    scalesElementCombFreq[[i]] <- str_replace(as.character(scalesElementCombFreq[[i]]), "1", colnames(scalesElementCombFreq)[i])
}

# collect Elements names for each combination
scalesElementCombFreq <- scalesElementCombFreq %>%
  unite("ExperienceCombination",c("Affect", "Behavior", "Cognition", "Desire"), na.rm = TRUE, sep = ", ") %>%
  mutate(ExperienceCombination = fct_reorder(ExperienceCombination, Frequency),
         Percentage = Frequency/nrow(dt.Scales.Included)*100,
         Affect = ifelse(grepl("Affect", ExperienceCombination, fixed = TRUE), 1,0),
         Behavior = ifelse(grepl("Behavior", ExperienceCombination, fixed = TRUE), 1,0),
         Cognition = ifelse(grepl("Cognition", ExperienceCombination, fixed = TRUE), 1,0),
         Desire = ifelse(grepl("Desire", ExperienceCombination, fixed = TRUE), 1,0)) %>%
  arrange(-Frequency)

scalesElementCombFreq %>%
  kbl(., 
      #label = "",
      caption = "Methodological Literature: Aspect Combinations",
      format = "html", 
      #linesep = "",
      #booktabs = T,
      digits = 2,
      align = c('l', rep('c', length(scalesElementCombFreq)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 20: Methodological Literature: Aspect Combinations
ExperienceCombination Frequency complexity Percentage Affect Behavior Cognition Desire
Behavior, Cognition 55 2 23.61 0 1 1 0
Affect, Behavior, Cognition 45 3 19.31 1 1 1 0
Affect, Behavior, Cognition, Desire 35 4 15.02 1 1 1 1
Affect, Cognition 21 2 9.01 1 0 1 0
Behavior 21 1 9.01 0 1 0 0
Cognition 18 1 7.73 0 0 1 0
Affect, Cognition, Desire 10 3 4.29 1 0 1 1
Behavior, Cognition, Desire 10 3 4.29 0 1 1 1
Cognition, Desire 10 2 4.29 0 0 1 1
Affect 3 1 1.29 1 0 0 0
Affect, Behavior 2 2 0.86 1 1 0 0
Affect, Desire 1 2 0.43 1 0 0 1
Behavior, Desire 1 2 0.43 0 1 0 1
Desire 1 1 0.43 0 0 0 1 
# bar plot frequencies
scalesABCDComb <- ggplot(scalesElementCombFreq, aes(x=ExperienceCombination, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    aes(y=Percentage, label = paste0(format(round(Percentage,2), nsmall=2),"% [N = ", Frequency, "]")),
    color = "grey14",
    size = 4,
    hjust = -.1,
    inherit.aes = TRUE
    ) +
  scale_y_continuous(limits = c(0, ceiling(max(scalesElementCombFreq$Percentage)*1.15)),
                     breaks = seq(0, ceiling(max(scalesElementCombFreq$Percentage)*1.15), 5))+
  labs(y = "Proportion of all scales [in %]",
       x = "Combination of Experience Aspects")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")

scalesABCDComb
Methodological Literature: Bar Graph Aspect Combinations

Figure 9: Methodological Literature: Bar Graph Aspect Combinations 

# summarize by aspect complexity
scalesComplexity <- scalesElementCombFreq %>%
  dplyr::select(complexity, Frequency) %>%
  group_by(complexity) %>%
  summarise(Frequency = sum(Frequency),
            Percentage = sum(Frequency)/nrow(dt.Scales.Included)*100) %>%
  ungroup() %>%
  mutate(complexity = as.factor(complexity),
         complexity = fct_reorder(complexity, Frequency)) %>%
  arrange(desc(Frequency))

# overall complexity mean and standard deviation
scalesComplexityAverage <- weighted.mean(as.numeric(as.character(scalesComplexity$complexity)), scalesComplexity$Frequency)
scalesComplexitySD <- wtd.var(x = as.numeric(as.character(scalesComplexity$complexity)), weights = scalesComplexity$Frequency)

# Table complexity distribution
scalesComplexity %>%
  kbl(., 
        #label = "",
        caption = "Methodological Literature: <br>Number of Aspects considered",
        format = "html", 
        digits = 2,
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(scalesComplexity)-1)))  %>%
  footnote(general = paste0("M = ",format(round(scalesComplexityAverage, 2), nsmall=2), ", SD = ", format(round(scalesComplexitySD, 2), nsmall=2))) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 21: Methodological Literature:
Number of Aspects considered
complexity Frequency Percentage
2 90 38.63
3 65 27.90
1 43 18.45
4 35 15.02
Note:
M = 2.39, SD = 0.91 
# barplot of complexity frequency
scalesComplexityBar <- ggplot(data=scalesComplexity, aes(x=complexity, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    #aes(label = paste0("N = ",Frequency)),
    aes(label = paste0(format(round(Percentage,2), nsmall=2), "%")),
    position=position_stack(vjust=0.5),
    color = "white",
    size = 4,
    vjust = 0.5
    ) +
  labs(title = "Numer of Aspects Considered",
       y = "Frequency across all Scales",
       x = "Number of Aspects Considered")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14", ),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size=10, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=10),
        axis.text.y = element_text(size=10),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        legend.position="none")

scalesComplexityBar
Methodological Literature: Bar Graph Number of Aspects considered

Figure 10: Methodological Literature: Bar Graph Number of Aspects considered 

# Numer of Aspects Considered for each element
scalesElementComplexity <- scalesElementCombFreq %>%
  gather(key = "Element", value = "ElementDum", Affect, Behavior, Cognition, Desire) %>%
  filter(ElementDum == 1) %>%
  group_by(Element) %>%
  summarise(n = sum(Frequency),
            avgComplexity = weighted.mean(x = complexity, w = Frequency),
            sdComplexity = wtd.var(x = complexity, weights = Frequency)) %>%
  ungroup() %>%
  mutate(seComplexity = sdComplexity/sqrt(n)) %>%
  arrange(-avgComplexity)

# Table Aspect complexity distribution
scalesElementComplexity %>%
  kbl(., 
        #label = "",
        caption = "Methodological Literature: <br>Number of Aspects considered with each aspect",
        col.names = c("Aspect", "N", "Mean", "Standard Deviation", "Standard Error"),
        format = "html", 
        digits = 2,
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(scalesElementComplexity)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 22: Methodological Literature:
Number of Aspects considered with each aspect
Aspect N Mean Standard Deviation Standard Error
Desire 68 3.31 0.66 0.08
Affect 117 3.04 0.61 0.06
Behavior 169 2.62 0.90 0.07
Cognition 204 2.57 0.77 0.05

We additionally inspected bi-variate relations between the individual aspects. We calculate the phi coefficient (for binary variables) together with the raw number of co-occurrences (see Table 23).

# make crossproduct matrix to condense co-occurrences (off-diagonals) and get frequencies (diagonals)
as.matrix(dt.Scales.Included %>% dplyr::select(Affect = AffectFinal, Behavior = BehaviorFinal,  Cognition = CognitionFinal, Desire = DesireFinal) %>%
            mutate_all(~replace(., is.na(.), 0))) %>%
  BinaryCor(., "pearson") %>%
  tibble::rownames_to_column(., var = "Aspect") %>%
  kbl(., 
        #label = "",
        caption = "Methodological Literature: <br>Aspects Bi-Variate Relations",
        format = "html", 
        linesep = "",
        booktabs = T,
        align = c('l', rep('c', ncol(.)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 23: Methodological Literature:
Aspects Bi-Variate Relations
Aspect Affect Behavior Cognition Desire
Affect N = 117 -0.06 0.22*** 0.22***
Behavior 82 N = 169 -0.09 -0.07
Cognition 111 145 N = 204 0.16*
Desire 46 46 65 N = 68 
# draw combined graph
cowplot::ggdraw() +
  cowplot::draw_plot(scalesABCDComb, x = 0, y = 0.3, width = 1, height = .7)+
  cowplot::draw_plot(scalesABCDBar, x = 0, y = 0, width = .55, height = .3) +
  cowplot::draw_plot(scalesComplexityBar, x = .55, y = 0, width = .45, height = .3) +
  cowplot::draw_plot_label(c("(A)", "(B)", "(C)"), c(0, 0, 0.55), c(1, 0.3, 0.3), size = 15)
Methodological Literature: Combined Bar Graphs

Figure 11: Methodological Literature: Combined Bar Graphs

Context

Culture

We also coded the cultural context the scales were validated in. We coded both the migrants’ country of origin as well as the country of the receivong society in which the study was conducted (see Figure 12).

# Frequencies of host country focus
scalesHostFreq <- dt.Scales.Included %>%
  dplyr::select(HostCountry) %>%
  mutate(country = strsplit(as.character(HostCountry), ", ")) %>% 
  unnest(country) %>%
  group_by(country) %>%
  summarise(Host = n()) %>%
  arrange(-Host)
  
# Frequencies of origin country focus
scalesOriginFreq <- dt.Scales.Included %>%
  dplyr::select(OriginCountry) %>%
  mutate(country = strsplit(as.character(OriginCountry), ", ")) %>% 
  unnest(country) %>%
  group_by(country) %>%
  summarise(Origin = n()) %>%
  arrange(-Origin)

# Frequencies of host country focus in wide format
scalesCountryFreqWide <- merge(scalesHostFreq, scalesOriginFreq, by = "country", all = TRUE) %>%
  mutate_if(is.numeric, funs(replace_na(., 0)))

# Frequencies of host country focus in long format
scalesCountryFreqLong <- scalesCountryFreqWide %>%
  melt(., id="country", value.name = "Frequency")
# bar plot country frequencies
ggplot(scalesCountryFreqLong , aes(x=reorder(country, Frequency), y=Frequency)) +
  geom_bar(stat="identity", fill="grey14") +
  labs(x = "Country") +
  coord_flip()+
  facet_wrap( ~ variable, nrow = 1) +
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Bar graph of scales counts for the individual host countries and countries of origin.

Figure 12: Bar graph of scales counts for the individual host countries and countries of origin.

Individual

The study sample a scale is validated in can be fairly important if one plans to use a scale for a context-specific phenomenon such as a cultural adaptation of two specific cultures. We, therefore, coded the type of sample the original authors used in their validation studies (see Figure 13).

# tally different samples
scalesSampleFreq <- as.data.frame(table(Sample = dt.Scales.Included$Sample)) %>%
  arrange(Freq) %>%    # First sort by val. This sort the dataframe but NOT the factor levels
  mutate(Sample=factor(Sample, levels=Sample)) # update factor levels
  
# barplot of sample frequency
ggplot(data=scalesSampleFreq, aes(x=Sample, y=Freq)) +
  geom_bar(stat="identity", fill="grey14") +
  ylab("Frequency") +
  ggtitle("Validation Sample") +
  coord_flip()+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Bar graph of the study samples used in the original validation studies.

Figure 13: Bar graph of the study samples used in the original validation studies.

The category general refers to a sampling strategy in which any consenting adult could participate in the study.

Situation

Acculturation can happen in different life domains (e.g., Arends-Tóth & Vijver, 2007; Zane & Mak, 2004). We coded which life domains the scales referred to within the scale items. (see Figure 14).

## prepare life domain data
# make vector with of unique life Domains
uniqueDomains <- purrr::discard(dt.Input$DomainTheory, is.na)
# make new dataframe withe only the life domain data from all scales
scalesDomains <- data.frame(lifeDomain = dt.Scales.Included$lifeDomain)
# add unique list of life domains as NA columns to the data frame
scalesDomains[,uniqueDomains] <- NA
# for each unique life domain [j] check whether it was included within the scale [i]
for (i in 1:nrow(scalesDomains)) {
  for (j in 1:length(uniqueDomains)) {
    scalesDomains[i,j+1] <- grepl(uniqueDomains[j], scalesDomains$lifeDomain[i], fixed = TRUE)*1
  }
}
# count how many life domains were coded for each scale
scalesDomains <- scalesDomains %>%
  mutate(nDomain = rowSums(across(contains(uniqueDomains))))
#make a frequency table for all the individual life domains
scalesDomainFreq <- colSums(scalesDomains %>% select(contains(uniqueDomains))) %>%
  data.frame(Domain=names(.), Frequency=., row.names=NULL) %>%
  mutate(Percentage = Frequency/nrow(scalesDomains)*100) %>%
  arrange(desc(Frequency))
# count frequencies of combinations
scalesDomainCombFreq <- scalesDomains %>%
  group_by(lifeDomain) %>%
  summarise(Frequency = n()) %>%
  mutate(Percentage = Frequency/nrow(scalesDomains)*100) %>%
  arrange(desc(Frequency))

scalesDomainUniqueComb <- format(round(sum(scalesDomainCombFreq$Percentage[scalesDomainCombFreq$Frequency==1]), 2), nsmall=2)
scalesDomainLess5 <- format(round(sum(scalesDomainCombFreq$Percentage[scalesDomainCombFreq$Percentage<5]), 2), nsmall=2)
# bar plot frequencies
scalesDomainsBar <- ggplot(scalesDomainFreq, aes(x=reorder(Domain, Frequency), y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    aes(y=Percentage, label = paste0(format(round(Percentage,2), nsmall=2),"% [N = ", Frequency, "]")),
    color = "grey14",
    size = 4,
    hjust = -.1,
    inherit.aes = TRUE
    ) +
  scale_y_continuous(limits = c(0, ceiling(max(scalesDomainFreq$Percentage)*1.15)),
                     breaks = seq(0, ceiling(max(scalesDomainFreq$Percentage)*1.15), 5))+
  labs(y = "Proportion of all theories [in %]",
       x = "Combination of Experience Aspects")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")

scalesDomainsBar
Empirical Literature: Bar Graph Situational Domains

Figure 14: Empirical Literature: Bar Graph Situational Domains

We then assessed domain clusters (i.e., combinations of domains that were assessed jointly). Unfortunately, a large number of scales focused on a unique combination of life domains (44.21%) and a large majority of domain combinations was used by less then five percent of the scales (85.41%, also see 24).

scalesDomainCombFreq %>%
  mutate(lifeDomain=str_replace_all(lifeDomain, ",", ", ")) %>%
  as.data.frame %>%
  kbl(., 
      #label = ,
      col.names = c("Life Domain Combination", "Frequency", "Percentage"),
      caption = "Empirical Literature: <br>Domain Combinations",
      #format = "html", 
      linesep = "",
      booktabs = T,
      align = c('l', rep('c', ncol(.)-1)))  %>%
  kableExtra::column_spec(1, width="50em") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
  scroll_box(width = "100%", height = "500px")
Table 24: Empirical Literature:
Domain Combinations
Life Domain Combination Frequency Percentage
general 34 14.5923
home/family, friends/aquaintances 8 3.4335
friends/aquaintances 7 3.0043
home/family, entertainment/media/news, recreation/sport/art, friends/aquaintances 6 2.5751
entertainment/media/news, friends/aquaintances 5 2.1459
home/family 5 2.1459
friends/aquaintances, community 4 1.7167
spirituality/religion, home/family, health/care, consumption/shopping, entertainment/media/news, education/school, recreation/sport/art, friends/aquaintances, community 4 1.7167
home/family, consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances, community 3 1.2876
home/family, entertainment/media/news 3 1.2876
home/family, entertainment/media/news, education/school, recreation/sport/art, friends/aquaintances, community 3 1.2876
community, politics 2 0.8584
consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances, community 2 0.8584
education/school 2 0.8584
education/school, friends/aquaintances 2 0.8584
education/school, recreation/sport/art, friends/aquaintances 2 0.8584
entertainment/media/news, friends/aquaintances, community 2 0.8584
home/family, consumption/shopping, entertainment/media/news, friends/aquaintances, community 2 0.8584
home/family, consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances 2 0.8584
home/family, consumption/shopping, entertainment/media/news, transport/travel, friends/aquaintances 2 0.8584
home/family, consumption/shopping, entertainment/media/news, work/money/finances, education/school, recreation/sport/art, friends/aquaintances 2 0.8584
home/family, consumption/shopping, entertainment/media/news, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, community 2 0.8584
home/family, consumption/shopping, entertainment/media/news, work/money/finances, friends/aquaintances, community 2 0.8584
home/family, consumption/shopping, recreation/sport/art, friends/aquaintances 2 0.8584
home/family, education/school 2 0.8584
home/family, entertainment/media/news, friends/aquaintances 2 0.8584
home/family, health/care, work/money/finances 2 0.8584
home/family, work/money/finances 2 0.8584
home/family, work/money/finances, education/school 2 0.8584
home/family, work/money/finances, friends/aquaintances, community 2 0.8584
recreation/sport/art, friends/aquaintances 2 0.8584
spirituality/religion, consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances 2 0.8584
spirituality/religion, home/family, consumption/shopping, education/school, friends/aquaintances 2 0.8584
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances, community 2 0.8584
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, work/money/finances, education/school, friends/aquaintances, community 2 0.8584
administration/legal matters, entertainment/media/news, transport/travel, friends/aquaintances 1 0.4292
administration/legal matters, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, community 1 0.4292
administration/legal matters, work/money/finances, friends/aquaintances 1 0.4292
community 1 0.4292
consumption/shopping 1 0.4292
consumption/shopping, education/school, transport/travel, recreation/sport/art 1 0.4292
consumption/shopping, entertainment/media/news, friends/aquaintances 1 0.4292
consumption/shopping, entertainment/media/news, friends/aquaintances, community 1 0.4292
consumption/shopping, entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances 1 0.4292
consumption/shopping, work/money/finances, education/school, transport/travel, recreation/sport/art, friends/aquaintances, community 1 0.4292
entertainment/media/news, recreation/sport/art 1 0.4292
entertainment/media/news, transport/travel, friends/aquaintances 1 0.4292
entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances 1 0.4292
health/care, entertainment/media/news, work/money/finances, friends/aquaintances, politics 1 0.4292
home/family, administration/legal matters, consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances, politics 1 0.4292
home/family, administration/legal matters, consumption/shopping, entertainment/media/news, work/money/finances, education/school, transport/travel, friends/aquaintances, community, politics 1 0.4292
home/family, administration/legal matters, consumption/shopping, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
home/family, administration/legal matters, entertainment/media/news, education/school, friends/aquaintances 1 0.4292
home/family, administration/legal matters, entertainment/media/news, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, politics, general 1 0.4292
home/family, administration/legal matters, work/money/finances, education/school 1 0.4292
home/family, administration/legal matters, work/money/finances, friends/aquaintances, community 1 0.4292
home/family, consumption/shopping, entertainment/media/news 1 0.4292
home/family, consumption/shopping, entertainment/media/news, education/school, friends/aquaintances, community 1 0.4292
home/family, consumption/shopping, entertainment/media/news, friends/aquaintances 1 0.4292
home/family, consumption/shopping, entertainment/media/news, transport/travel, recreation/sport/art, friends/aquaintances, community 1 0.4292
home/family, consumption/shopping, entertainment/media/news, work/money/finances, education/school, recreation/sport/art 1 0.4292
home/family, consumption/shopping, entertainment/media/news, work/money/finances, education/school, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
home/family, consumption/shopping, entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances, community 1 0.4292
home/family, consumption/shopping, friends/aquaintances, community 1 0.4292
home/family, consumption/shopping, recreation/sport/art, community 1 0.4292
home/family, consumption/shopping, transport/travel, friends/aquaintances, community 1 0.4292
home/family, consumption/shopping, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
home/family, education/school, recreation/sport/art, friends/aquaintances 1 0.4292
home/family, entertainment/media/news, education/school, community 1 0.4292
home/family, entertainment/media/news, education/school, recreation/sport/art, politics 1 0.4292
home/family, entertainment/media/news, friends/aquaintances, community 1 0.4292
home/family, entertainment/media/news, recreation/sport/art, friends/aquaintances, community 1 0.4292
home/family, entertainment/media/news, work/money/finances, education/school, friends/aquaintances 1 0.4292
home/family, entertainment/media/news, work/money/finances, education/school, friends/aquaintances, community 1 0.4292
home/family, entertainment/media/news, work/money/finances, education/school, friends/aquaintances, community, politics 1 0.4292
home/family, entertainment/media/news, work/money/finances, education/school, recreation/sport/art, friends/aquaintances 1 0.4292
home/family, entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances 1 0.4292
home/family, friends/aquaintances, community 1 0.4292
home/family, health/care, administration/legal matters, consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
home/family, health/care, consumption/shopping, entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances 1 0.4292
home/family, health/care, consumption/shopping, entertainment/media/news, work/money/finances, transport/travel, recreation/sport/art, friends/aquaintances 1 0.4292
home/family, health/care, consumption/shopping, entertainment/media/news, work/money/finances, transport/travel, recreation/sport/art, friends/aquaintances, community 1 0.4292
home/family, health/care, entertainment/media/news, education/school, transport/travel, recreation/sport/art, friends/aquaintances, politics 1 0.4292
home/family, health/care, work/money/finances, education/school, friends/aquaintances 1 0.4292
home/family, recreation/sport/art, friends/aquaintances 1 0.4292
home/family, transport/travel, friends/aquaintances, community 1 0.4292
home/family, work/money/finances, education/school, community 1 0.4292
home/family, work/money/finances, education/school, friends/aquaintances, politics 1 0.4292
home/family, work/money/finances, education/school, recreation/sport/art, friends/aquaintances 1 0.4292
home/family, work/money/finances, politics 1 0.4292
recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, administration/legal matters, consumption/shopping, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, consumption/shopping, entertainment/media/news, education/school, friends/aquaintances 1 0.4292
spirituality/religion, entertainment/media/news, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, health/care, administration/legal matters, consumption/shopping, entertainment/media/news, work/money/finances, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, administration/legal matters, consumption/shopping, education/school, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, administration/legal matters, consumption/shopping, entertainment/media/news, work/money/finances, education/school, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, administration/legal matters, consumption/shopping, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, administration/legal matters, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, consumption/shopping, education/school 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, recreation/sport/art, friends/aquaintances 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, work/money/finances 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, work/money/finances, education/school, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances, politics 1 0.4292
spirituality/religion, home/family, consumption/shopping, entertainment/media/news, work/money/finances, transport/travel, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, consumption/shopping, recreation/sport/art, community 1 0.4292
spirituality/religion, home/family, consumption/shopping, recreation/sport/art, friends/aquaintances 1 0.4292
spirituality/religion, home/family, consumption/shopping, work/money/finances, education/school, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, consumption/shopping, work/money/finances, education/school, friends/aquaintances, politics 1 0.4292
spirituality/religion, home/family, consumption/shopping, work/money/finances, transport/travel, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, entertainment/media/news, education/school, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, entertainment/media/news, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, entertainment/media/news, friends/aquaintances, politics 1 0.4292
spirituality/religion, home/family, entertainment/media/news, recreation/sport/art, friends/aquaintances 1 0.4292
spirituality/religion, home/family, entertainment/media/news, transport/travel, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, entertainment/media/news, work/money/finances, education/school, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, entertainment/media/news, work/money/finances, education/school, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, home/family, entertainment/media/news, work/money/finances, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, health/care, community 1 0.4292
spirituality/religion, home/family, health/care, consumption/shopping 1 0.4292
spirituality/religion, home/family, health/care, consumption/shopping, community 1 0.4292
spirituality/religion, home/family, health/care, consumption/shopping, entertainment/media/news, education/school, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, health/care, consumption/shopping, entertainment/media/news, work/money/finances, education/school, transport/travel, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, health/care, consumption/shopping, work/money/finances, education/school, recreation/sport/art, community 1 0.4292
spirituality/religion, home/family, health/care, entertainment/media/news, education/school, transport/travel, recreation/sport/art, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, health/care, work/money/finances, education/school, friends/aquaintances 1 0.4292
spirituality/religion, home/family, work/money/finances, education/school 1 0.4292
spirituality/religion, home/family, work/money/finances, education/school, recreation/sport/art 1 0.4292
spirituality/religion, home/family, work/money/finances, friends/aquaintances 1 0.4292
spirituality/religion, home/family, work/money/finances, friends/aquaintances, community 1 0.4292
spirituality/religion, home/family, work/money/finances, recreation/sport/art, friends/aquaintances, community, politics 1 0.4292
spirituality/religion, recreation/sport/art, friends/aquaintances, community 1 0.4292
work/money/finances 1 0.4292
work/money/finances, education/school, recreation/sport/art, community 1 0.4292

We additionally inspected bi-variate relations between the individual life domains. We calculate the phi coefficient (for binary variables) together with the raw number of co-occurrences (see Table 25 and Figure 15).

# make crossproduct matrix to condense co-occurrences (off-diagonals) and get frequencies (diagonals)
as.matrix(scalesDomains %>% dplyr::select(contains(uniqueDomains), -"N/A") %>%
            mutate_all(~replace(., is.na(.), 0))) %>%
  BinaryCor(., "pearson") %>%
  tibble::rownames_to_column(., var = "Domain") %>%
  kbl(., 
        #label = "",
        caption = "Empirical Literature: <br>Domains Bi-Variate Relations",
        format = "html", 
        linesep = "",
        booktabs = T,
        align = c('l', rep('c', ncol(.)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 25: Empirical Literature:
Domains Bi-Variate Relations
Domain spirituality/religion home/family health/care administration/legal matters consumption/shopping entertainment/media/news work/money/finances education/school transport/travel recreation/sport/art friends/aquaintances community politics general
spirituality/religion N = 57 0.30*** 0.26*** 0.07 0.38*** 0.17* 0.20** 0.24*** 0.13 0.25*** 0.19** 0.32*** 0.21** -0.24***
home/family 50 N = 145 0.19** 0.05 0.33*** 0.31*** 0.31*** 0.27*** 0.10 0.25*** 0.27*** 0.24*** 0.16* -0.51***
health/care 13 20 N = 22 0.02 0.20** 0.12 0.14* 0.14* 0.17** 0.14* 0.04 0.14* 0.06 -0.14*
administration/legal matters 6 12 2 N = 17 0.11 0.01 0.14* 0.11 0.33*** 0.15* 0.16* 0.16* 0.39*** -0.07
consumption/shopping 38 68 14 9 N = 81 0.37*** 0.14* 0.12 0.30*** 0.39*** 0.29*** 0.34*** 0.16* -0.31***
entertainment/media/news 34 83 14 8 57 N = 105 0.11 0.11 0.13* 0.37*** 0.46*** 0.24*** 0.13* -0.36***
work/money/finances 26 59 11 9 31 37 N = 69 0.38*** 0.08 0.17* 0.14* 0.19** 0.21** -0.25***
education/school 28 57 11 8 30 37 39 N = 69 0.05 0.21** 0.12 0.19** 0.15* -0.25***
transport/travel 10 19 6 8 19 16 10 9 N = 25 0.24*** 0.22*** 0.24*** 0.29*** -0.15*
recreation/sport/art 34 69 13 11 52 61 35 37 18 N = 89 0.39*** 0.28*** 0.19** -0.31***
friends/aquaintances 47 111 16 16 69 95 53 52 24 80 N = 155 0.35*** 0.16* -0.57***
community 37 68 13 11 49 53 36 36 18 49 77 N = 88 0.22*** -0.33***
politics 14 24 4 10 16 18 16 14 10 18 25 19 N = 29 -0.12
general 0 1 0 1 0 1 1 1 0 1 1 0 1 N = 35 
# make crossproduct matrix to condense co-occurrences (off-diagonals) and get frequencies (diagonals)
X <- as.matrix(scalesDomains %>% dplyr::select(-c("lifeDomain", "N/A", "nDomain")))
 out <- crossprod(X)  # Same as: t(X) %*% X
 #diag(out) <- 0       # remove frequencies on diagonals

 # information on the concepts themselves
 nodes <- data.frame(id    = colnames(out), 
                     title = paste0(colnames(out), "<br>Frequency: ", diag(out)), 
                     value = diag(out),
                     size  = diag(out),
                     shape = "dot")

 # information on the co-occurrences
 edges           <- as.data.frame(t(combn(colnames(out),2)))
 colnames(edges) <- c('from','to')
 edges$width     <- NA
 for (i in 1:nrow(edges)) {
     edges$width[i] <- out %>%
      as.data.frame(.) %>%
      rownames_to_column('dim') %>%
      filter(dim == edges$to[i]) %>%
      dplyr::select(any_of(edges$from[i])) %>%
      as.numeric(.)
}
edges$label <- edges$width
edges$title <- paste0(edges$from, " - ", edges$to, "<br>Co-occurences: ", edges$width)

# plot network graph
visNetwork(nodes, edges %>% mutate(width = scales::rescale(edges$width, to=c(1,20))), 
           heigth = "100%", width = "100%", main = "Co-occurences of Life Domains as Network") %>%
            visIgraphLayout(layout = "layout_in_circle") %>%
            visNodes(
                shape = "dot",
                color = list(
                    background = "#0085AF",
                    border = "#013848",
                    highlight = "#FF8000"
                )
            ) %>%
            visEdges(
                shadow = FALSE,
                color = list(color = "#0085AF", highlight = "#C62F4B")
            ) %>%
            visOptions(highlightNearest = list(enabled = T, degree = 1, hover = T), 
                       nodesIdSelection = list(main = "Select variable")) %>% 
            visInteraction(keyboard = TRUE, tooltipDelay = 0) %>%
            visLayout(randomSeed = 11)

Figure 15: Network graph of the domain frequencies and co-occurences. The nodes (i.e., circles) represent the life domains so that the size of the circle indicates the number of times the domain was coded and the edges (i.e., connections) represent the co-occurences so that the width of the line indicates how often the domains were measured together in one scale.

Process

To assess the temporal focus of the validated scales we also checked whether scales were validated for samples prior to migration, post-migration, or both (see Table 26).

# table of the migration time focus
dt.Scales.Included %>%
  dplyr::select(MigrationTime) %>%
  mutate(MigrationTime = replace_na(MigrationTime, "N/A")) %>%
  group_by(MigrationTime) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Methodological Literature: <br>Migration Time",
      format = "html",
      digits = 2,
      col.names = c("Migration Time", "Frequency", "Percentage")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 26: Methodological Literature:
Migration Time
Migration Time Frequency Percentage
post 217 93.13
N/A 15 6.44
potential 1 0.43

Content

While a discussion of all the topics addressed by the included scales lies beyond the scope of this study, we would like to describe some of the larger patterns authors have focused on. To that aim, we ran a machine learning topic modeling procedure on the items of the scales to identify content topics.

For the topic modeling of the acculturation scales, we particularly used the scale items in a Latent Dirichlet Allocation (LDA) analysis, an unsupervised machine learning method common within the natural language processing literature. The analysis essentially extracts sets of terms that tended to occur together, assuming that scales that measure a specific topic have more words that relate to the topic than scales that measure other topics. We followed the procedures outlined by Schweinberger (2022).

In particular, we:

  1. Transformed the items as a Text Corpus object
  2. Pre-processed the text data
    1. all words in lower case
    2. English stop words removed
    3. punctuation removed
    4. numerals removed
    5. word stems extracted
    6. superfluous white spaces removed
  3. transformed the text corpus into a Document Term Matrix
  4. determined the appropriate number of topics to extract
  5. ran the LDA with varying alphas
  6. extracted the most probable topics
scaleItemDescr <- psych::describe(as.numeric(dt.Scales.Included$NItems))
scaleDomainDescr <- psych::describe(as.numeric(str_count(dt.Scales.Included$lifeDomain, ',')+1))


# Based on 
# Schweinberger, Martin. 2022. Topic Modeling with R. Brisbane: The University of Queensland. url: https://slcladal.github.io/topicmodels.html (Version 2022.05.21).
library(DT)
library(tm)
library(topicmodels)
library(reshape2)
library(ggplot2)
library(wordcloud)
library(pals)
library(SnowballC)
library(lda)
library(ldatuning)
library(flextable)
textdata <- base::readRDS(url("https://slcladal.github.io/data/sotu_paragraphs.rda", "rb"))
textdata <- dt.Scales.Included %>%
  select(
    Scale,
    CitationKey,
    Item
  ) %>%
  mutate(
    doc_id = row_number(),
    text = gsub("[\r\n]", "", Item)
  ) %>%
  select(
    -Item
  ) %>% 
  as.data.frame

# load stopwords
english_stopwords <- readLines("https://slcladal.github.io/resources/stopwords_en.txt", encoding = "UTF-8")

# create corpus object
corpus <- Corpus(DataframeSource(textdata))
# Preprocessing chain
processedCorpus <- tm_map(corpus, content_transformer(tolower))
processedCorpus <- tm_map(processedCorpus, removeWords, english_stopwords)
processedCorpus <- tm_map(processedCorpus, removePunctuation, preserve_intra_word_dashes = TRUE)
processedCorpus <- tm_map(processedCorpus, removeNumbers)
processedCorpus <- tm_map(processedCorpus, stemDocument, language = "en")
processedCorpus <- tm_map(processedCorpus, stripWhitespace)

# compute document term matrix with terms >= minimumFrequency
minimumFrequency <- 5
DTM <- DocumentTermMatrix(processedCorpus, control = list(bounds = list(global = c(minimumFrequency, Inf))))
# have a look at the number of documents and terms in the matrix
dim(DTM)

# due to vocabulary pruning, we have empty rows in our DTM
# LDA does not like this. So we remove those docs from the
# DTM and the metadata
sel_idx <- slam::row_sums(DTM) > 0
DTM <- DTM[sel_idx, ]
textdata <- textdata[sel_idx, ]

# create models with different number of topics
result <- ldatuning::FindTopicsNumber(
  DTM,
  topics = seq(from = 2, to = 20, by = 1),
  metrics = c("CaoJuan2009",  "Deveaud2014"),
  method = "Gibbs",
  control = list(seed = 77),
  verbose = TRUE
)

FindTopicsNumber_plot(result)

# number of topics
K <- 20
# set random number generator seed
set.seed(9161)
# compute the LDA model, inference via 1000 iterations of Gibbs sampling
topicModel <- LDA(DTM, K, method="Gibbs", control=list(iter = 500, verbose = 25))

# have a look a some of the results (posterior distributions)
tmResult <- posterior(topicModel)
# format of the resulting object
#attributes(tmResult)
# lengthOfVocab
#nTerms(DTM) 
# get beta from results
beta <- tmResult$terms   
#dim(beta)  
# rows in beta sum to 1
#rowSums(beta)    
# size of collection
#nDocs(DTM)               
# for every document we have a probability distribution of its contained topics
theta <- tmResult$topics 
#dim(theta)               # nDocs(DTM) distributions over K topics

exampleTermData <- terms(topicModel, 10)
exampleTermData[, 1:8]

top5termsPerTopic <- terms(topicModel, 5)
topicNames <- apply(top5termsPerTopic, 2, paste, collapse=" ")

# visualize topics as word cloud
topicToViz <- 11 # change for your own topic of interest
topicToViz <- grep('feel', topicNames)[1] # Or select a topic by a term contained in its name
# select to 40 most probable terms from the topic by sorting the term-topic-probability vector in decreasing order

topTopicTerms <- list()
topTopicTermsPlot <- list()
mycolors <- brewer.pal(8, "Dark2")

for (i in 1:K) {
  top40terms <- sort(tmResult$terms[i,], decreasing=TRUE)[1:40]
  topTopicTerms[[i]] <- top40terms
  topTopicTermsPlot[[i]] <- ggwordcloud(names(top40terms), top40terms, random.order = FALSE, color = mycolors)
}

#grid.arrange(grobs = topTopicTermsPlot, ncol = 5)

# focus on unique topics
set.seed(9161)
topicModel2 <- LDA(DTM, K, method="Gibbs", control=list(iter = 500, verbose = 25, alpha = 0.2))
tmResult <- posterior(topicModel2)
theta <- tmResult$topics
beta <- tmResult$terms
topicNames <- apply(terms(topicModel2, 5), 2, paste, collapse = " ")  # reset topicnames


# re-rank top topic terms for topic names
topicNames <- apply(lda::top.topic.words(beta, 5, by.score = T), 2, paste, collapse = " ")


## APROACH 1
# What are the most probable topics in the entire collection?
topicProportions <- colSums(theta) / nDocs(DTM)  # mean probabilities over all paragraphs
names(topicProportions) <- topicNames     # assign the topic names we created before
sort(topicProportions, decreasing = TRUE) # show summed proportions in decreased order

soP <- sort(topicProportions, decreasing = TRUE)
#paste(round(soP, 5), ":", names(soP))

## APPROACH 2
countsOfPrimaryTopics <- rep(0, K)
names(countsOfPrimaryTopics) <- topicNames
for (i in 1:nDocs(DTM)) {
  topicsPerDoc <- theta[i, ] # select topic distribution for document i
  # get first element position from ordered list
  primaryTopic <- order(topicsPerDoc, decreasing = TRUE)[1] 
  countsOfPrimaryTopics[primaryTopic] <- countsOfPrimaryTopics[primaryTopic] + 1
}
sort(countsOfPrimaryTopics, decreasing = TRUE)

so <- sort(countsOfPrimaryTopics, decreasing = TRUE)
#paste(so, ":", names(so))

# SCALES:
# get mean topic proportions per decade
topic_proportion_per_scale <- aggregate(theta, by = list(scale = textdata$CitationKey), mean)
# set topic names to aggregated columns
colnames(topic_proportion_per_scale)[2:(K+1)] <- topicNames
# reshape data frame
vizDataFrame <- melt(topic_proportion_per_scale, id.vars = "scale")
# plot topic proportions per decade as bar plot
ggplot(vizDataFrame, aes(x=scale, y=value, fill=variable)) + 
  geom_bar(stat = "identity") + ylab("proportion") + 
  scale_fill_manual(values = paste0(alphabet(20), "FF"), name = "scale") + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

soP %>%
  kbl(., 
      caption = "LDA: Proportions of all 20 topics with the five most common terms extracted",
      format = "html",
      digits = 2) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 27: LDA: Proportions of all 20 topics with the five most common terms extracted
x
languag speak english read listen 0.12
cultur feel heritag ident part 0.08
peopl feel thing don’ treat 0.07
countri live life nation host 0.07
ethnic group nation member import 0.07
famili children import parent person 0.06
american vietnames food eat speak 0.05
social friendship custom domain educ 0.05
american nativ asian english equal 0.05
parent school friend work close 0.04
local deal understand social make 0.04
particip state unit difficult belong 0.04
spanish mexican english speak respons 0.04
live cloth australian boy famili 0.04
indian food eat asian peopl 0.04
russian isra hebrew behavior cultur 0.03
black chines peopl white prefer 0.03
cultur african activ foreign communiti 0.03
korean canadian societi assimil separ 0.02
particip danc answer hard collect 0.02

Empirical Literature

Descriptives

Publication Type

A majority of the results were journal articles but we also reviewed a number of theses and book chapters (see Table 28).

dt.Empirical.included %>%
  dplyr::select(PublicationType) %>%
  group_by(PublicationType) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Empirical Literature: <br>Type of Publication",
      format = "html",
      digits = 2) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 28: Empirical Literature:
Type of Publication
PublicationType Frequency Percentage
journalArticle 452 85.93
thesis 68 12.93
bookSection 6 1.14

Data Type

A majority of the empirical papers were quantitative assessments of acculturation (for an overview of the data collection types see Table 29).

dt.Empirical.included %>%
  dplyr::select(Method) %>%
  group_by(Method) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Empirical Literature: <br>Type of Data Collection",
      format = "html",
      digits = 2,
      col.names = c("Data Type", "Frequency", "Percentage")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 29: Empirical Literature:
Type of Data Collection
Data Type Frequency Percentage
quantitative 464 88.21
mixed method 39 7.41
qualitative 20 3.80
Review 3 0.57

Sample

majority, year of publication

We firstly assessed how many of the scale validations included the dominant group was measured as well (see Table 30).

# Count whether validations included Majority
empiricalMajFreq <- dt.Empirical.included %>%
  dplyr::select(IncludesMajority) %>%
  filter(!is.na(IncludesMajority)) %>%
  mutate(`Includes Majority` = recode_factor(.$IncludesMajority, `0` = "no", `1` = "yes")) %>%
  group_by(`Includes Majority`) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100)

empiricalMajFreq %>%
  kbl(., 
      #label = "",
      caption = "Empirical Literature: <br>Dominant Group Included Frequency",
      format = "html", 
      #linesep = "",
      #booktabs = T,
      digits = 2,
      align = c('l', rep('c', ncol(.)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 30: Empirical Literature:
Dominant Group Included Frequency
Includes Majority Frequency Percentage
no 447 85.31
yes 77 14.69

Of the scale we included we also plotted the publication years of the scale validations in order to gain an understanding of the interest in scale development over time (see Figure 16).

# marginal histogram of number of manuscripts per year (with minimal theme)
empiricalHist <- dt.Empirical.included %>%
  dplyr::select(year) %>%
  mutate(year = as.POSIXct(as.character(year), format = "%Y")) %>%
  ggplot(., aes(x=year)) + 
  geom_histogram(bins = length(table(dt.Empirical.included$year)), fill = "grey14") +
  labs(title = "Year of Publication [Empirical works]",
       y = "Number of Studies",
       x = "Year") +
  #scale_y_continuous(breaks = seq(0,20,10)) +
  scale_x_datetime(breaks = as.POSIXct(as.character(seq(1920,2020,10)), format = "%Y"), date_labels = "%Y") +
  theme_Publication() +
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_blank(), #element_text(size=16, face="bold", hjust = 0.5),
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        axis.ticks.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.x = element_blank(),
        axis.line.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        plot.margin = unit(c(5, 5, 0, 5), "pt"),
        legend.position="none")

# Cumulative Sum of articles published over time
empiricalYearCum <- data.frame(table(Year = dt.Empirical.included$year)) %>%
  mutate(Year = as.POSIXct(Year, format = "%Y"),
         CumSum = cumsum(Freq)) %>%
  ggplot(., aes(x=Year, y=CumSum, group=1)) +
  geom_point() +
  geom_line() +
  scale_y_continuous(breaks = seq(0,550,50)) +
  scale_x_datetime(breaks = as.POSIXct(as.character(seq(1920,2020,10)), format = "%Y"), date_labels = "%Y") +
  labs(title = "Year of Publication [Empirical works]",
       y = "Cumulative Sum",
       x = "Year") +
  theme_Publication() +
  theme(strip.background = element_rect(fill="grey14", color="grey14", ),
        axis.title.x = element_text(size=14, face="bold"),
        axis.title.y = element_text(size=14, face="bold"),
        plot.title = element_blank(), #element_text(size=16, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=14),
        axis.text.y = element_text(size=14),
        #panel.grid.major.x = element_blank(),
        #panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        plot.margin = unit(c(0, 5, 5, 5), "pt"),
        legend.position="none")

# combine the two plots
cowplot::plot_grid(empiricalHist, empiricalYearCum, nrow=2, align = "v", rel_heights = c(1/5, 4/5))
Year of Publication [Empirical Works]. The graph shows the cumulative sum in the main graph as well as the number of works published over time in the marginal graph.

Figure 16: Year of Publication [Empirical Works]. The graph shows the cumulative sum in the main graph as well as the number of works published over time in the marginal graph.

We additionally assessed empirical developments in terms of publication type (see Figure 17) and data collection type (see Figure 18).

ggplot(dt.Empirical.included, aes(x=year, linetype = PublicationType, shape = PublicationType)) + 
  geom_density(aes(y = ..count..)) +
  #geom_line(stat='count') +
  geom_point(stat='count', size = .9) +
  ylab("Number of Publications")+
  xlab("Year") +
  ggtitle("Publication Year by Publication Type (Density Plot)")+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="bottom")
Density plot of the yearly publication frequency by type of publication.

Figure 17: Density plot of the yearly publication frequency by type of publication. 

ggplot(dt.Empirical.included, aes(x=year, linetype = Method, shape = Method)) + 
  geom_density(aes(y = ..count..)) +
  #geom_line(stat='count') +
  geom_point(stat='count', size = .9) +
  ylab("Number of Results")+
  xlab("Year") +
  ggtitle("Publication Year by Data Type (Density Plot)")+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="bottom")
Density plot of the yearly publication frequency by data collection type.

Figure 18: Density plot of the yearly publication frequency by data collection type.

Focus

Term used, focus of the paper, variable type, analysis

The field of acculturation has been using a variety of terms to describe the process of cultural adaptation. We list the terms the authors used in their paper to refer to cultural adaptation in Table 31.

dt.Empirical.included %>%
  dplyr::select(term) %>%
  mutate(term = tolower(term)) %>%
  group_by(term) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Empirical Literature: Terms used",
      format = "html",
      digits = 2,
      col.names = c("Term", "Frequency", "Percentage")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
   scroll_box(width = "100%", height = "500px")
Table 31: Empirical Literature: Terms used
Term Frequency Percentage
acculturation 388 73.76
integration 21 3.99
assimilation 18 3.42
social integration 11 2.09
acculturation attitudes 9 1.71
adaptation 9 1.71
adjustment 8 1.52
aculturative stress 6 1.14
sociocultural adjustment 6 1.14
psychological adjustment 5 0.95
acculturation strategies 4 0.76
ethnic identity 4 0.76
acculturation orientation 3 0.57
acculturation preferences 3 0.57
emotional acculturation 3 0.57
acculturation patterns 2 0.38
americanization 2 0.38
cultural adaptation 2 0.38
acculturation and enculturation 1 0.19
acculturative adjustment 1 0.19
acculturative orientation 1 0.19
cross-cultural adaptation 1 0.19
cross-cultural adjustment 1 0.19
cultural identification 1 0.19
cultural identity change 1 0.19
cultural orientation 1 0.19
cultural transition 1 0.19
cultural transmission 1 0.19
culturing 1 0.19
economic integration 1 0.19
immigation success 1 0.19
immigration transition 1 0.19
intercultural adjustment 1 0.19
linguistic acculturation 1 0.19
mental health 1 0.19
migration 1 0.19
national identity 1 0.19
psychological and sociocultural adjustment 1 0.19
psychological flourishing 1 0.19
sociocultural adaptation 1 0.19

For the empirical works we also coded the main focus of the papers. The wordcloud of the topics illustrates that although a majority of articles also have acculturation as their main focus, health and adjustment are also fields that measured acculturation in their empirical works (see Figure 19).

# remove duplicate domains
UniqueDomains <- dt.Domains %>% 
  filter(Duplicate == "Unique")

# dataframe to compile domain frequency for Review Topics
empiricalTopicFreq <- data.frame(UniqueDomains, Frequency = NA)

# count domain frequency in dt.Scales.Included
for (i in 1:nrow(UniqueDomains)) {
  empiricalTopicFreq$Frequency[i] <- length(grep(UniqueDomains$Domain[i], dt.Empirical.included$domainPaper, value = T))
}

# prepare for ggplot
empiricalTopicFreq <- empiricalTopicFreq %>%
  dplyr::select(Domain, Frequency) %>%
  filter(Frequency > 0) %>%
  arrange(Frequency) %>%    # First sort by val. This sort the dataframe but NOT the factor levels
  mutate(Domain=factor(Domain, levels=Domain)) # update factor levels

# Word cloud frequencies
set.seed(7) # for reproducibility 
wordcloud(words = empiricalTopicFreq$Domain, freq = empiricalTopicFreq$Frequency, 
          min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))
Wordcloud of the article foci in the empirical results.

Figure 19: Wordcloud of the article foci in the empirical results.

Measure Used

We list the measures used by the authors in Table 32. Note that a majority of the measurements are not previously standardized and are not shared across articles.

dt.Empirical.included %>%
  dplyr::select(MeasureDefinition) %>%
  mutate(MeasureDefinition = tolower(MeasureDefinition)) %>%
  group_by(MeasureDefinition) %>%
  summarise(Frequency = n(),
            Percentage = Frequency / nrow(.) * 100) %>%
  filter(MeasureDefinition != "") %>%
  arrange(desc(Frequency)) %>%
  kbl(
    .,
    caption = "Empirical Literature: Measure used",
    format = "html",
    digits = 2,
    col.names = c("Measure", "Frequency", "Percentage")
  ) %>%
  kable_classic(
    full_width = F,
    lightable_options = "hover",
    html_font = "Cambria"
  ) %>%
  scroll_box(width = "100%", height = "500px")
Table 32: Empirical Literature: Measure used
Measure Frequency Percentage
vancouver index of acculturation (via; ryder et al., 2000) 17 3.23
language use [own] 15 2.85
language, identity, and behavioral acculturation scale 13 2.47
stephenson multigroup acculturation scale 11 2.09
acculturation orientation/acculturation strategy 10 1.90
acculturation rating scale for mexican-americans - revised (arsma-ii) 10 1.90
short acculturation scale for hispanics (marin, sabogal, marin, otero-sabogal, & perez-stable, 1987) 10 1.90
vancouver index of acculturation 10 1.90
abbreviated multidimensional acculturation scale 7 1.33
brief acculturation rating scale for mexican americans–ii (arsma ii- sf) 7 1.33
suinn-lew asian self identity acculturation scale 7 1.33
acculturation rating scale for mexican americans-ii 6 1.14
iranian acculturation scale 6 1.14
suinn-lew asian self-identity accultururation scale (sl-asia) 6 1.14
asian american multidimensional acculturation scale (aamas; chung, kim, & abreu, 2004) 5 0.95
proxy acculturation scale (pas-3 and pas-4) 5 0.95
relative acculturation extended model scale (real and ideal acculturation strategies and attitudes [raem]) 5 0.95
acculturation attitudes (arends-tóth & van de vijver, 2007) 4 0.76
acculturation attitudes scale 4 0.76
bicultural involvement questionnaire 4 0.76
bidimensional acculturation scale for hispanics 4 0.76
immigrant acculturation scale 4 0.76
language proficiency 4 0.76
behavioral acculturation scale 3 0.57
east asian acculturation measure 3 0.57
identification [own] 3 0.57
language proficiency [own] 3 0.57
lowlands acculturation scale 3 0.57
mutual intercultural relations in plural societies - acculturation attitudes scale 3 0.57
psychological acculturation scale (tropp et al., 1999) 3 0.57
short acculturation scale for hispanics 3 0.57
abbreviated multidimensional acculturation scale (amas) 2 0.38
acculturation attitude scale (sam, 1995) 2 0.38
acculturation attitudes 2 0.38
acculturation attitudes (icsey) 2 0.38
acculturation attitudes (icsey) [assimilation subscale] 2 0.38
acculturation index 2 0.38
acculturation index (ai; ward & kennedy, 1994) 2 0.38
acculturation strategies (mancini, & bottura, 2014); acculturation attitudes (mancini, & bottura, 2014) 2 0.38
acculturation strategies scale (modified; kosic, 1998) 2 0.38
acculturation, habits and interests multicultural scale for adolescents (ahimsa) 2 0.38
adapted bidimensional acculturation scale for hispanics 2 0.38
adopted language, identity, and behavioral acculturation scale 2 0.38
assimilation scale (cernovsky, 1990) 2 0.38
bidimensional acculturation scale 2 0.38
chis acculturation score 2 0.38
cultual adaptation korean elderly 2 0.38
cultural lifestyle inventory 2 0.38
emotional acculturation 2 0.38
general acculturation attitudes 2 0.38
identity subscale from language, identity, and behavioral acculturation scale 2 0.38
language proficiency [own], language use [own] 2 0.38
language proficiency [own]; language use [own] 2 0.38
marital assimilation [own] 2 0.38
multidimensional acculturative stress scale 2 0.38
multigroup ethnic identity measure 2 0.38
multigroup ethnic identity measure revised 2 0.38
proxy acculturation scale (pas-3) 2 0.38
relative acculturation extended model scale (real and ideal acculturation strategies and attitudes) 2 0.38
social readjustment rating scale 2 0.38
speaking english at home [own] 2 0.38
2 measures - attitudes (intrinsic) & extrinsic cultural traits 1 0.19
abbreviated acculturation modes for koreans (lee, sobal, and frongillo, 2003) 1 0.19
abbreviated multidimensional acculturation scale (amas); multigroup ethnic identity measure (meim) 1 0.19
abbreviated multidimensional acculturation scale (amas); social connectedness in mainstream society (scmn) scale; social connectedness in the ethnic community (sceth) scale 1 0.19
abridged general ethnicity questionnaire—american and chinese version 1 0.19
abridged general ethnicity questionnaire—american version (geqa) 1 0.19
acceptance of american cultural values; affinity for korean cultural identity 1 0.19
accultuation orientation (arends-tóth & van de vijver, 2004) 1 0.19
acculturation attitude scale (aas-16); sociocultural adjustment scale - revised (scas-r) 1 0.19
acculturation attitude scale (berry, 1989) 1 0.19
acculturation attitude scale (kim, 1988) 1 0.19
acculturation attitudes scale; behavioral adaptation scale 1 0.19
acculturation experiences 1 0.19
acculturation in australia 1 0.19
acculturation index (ai; ward & kennedy, 1994); identification 1 0.19
acculturation index (nesarc) 1 0.19
acculturation indicators (needham, et al., 2017) 1 0.19
acculturation intergenerational conflict scale (aic) 1 0.19
acculturation orientation (based on horenczyk, 1996, 2000) 1 0.19
acculturation orientation (demes, & geeraert, 2014) 1 0.19
acculturation preference [unclear], intergroup contact [unclear] 1 0.19
acculturation preferences 1 0.19
acculturation preferences (christ et al., 2013) 1 0.19
acculturation preferences [own] 1 0.19
acculturation rating scale for arab-american-ii 1 0.19
acculturation rating scale for mexican americans-ii (language sub-scale) 1 0.19
acculturation rating scale for mexican-americans - revised (arsma-ii); mexican american cultural values scale (macvs) 1 0.19
acculturation scale (acc) 1 0.19
acculturation scale (cheung, 1995) 1 0.19
acculturation scale (wang, 2007) 1 0.19
acculturation scale for asian indians (asai) 1 0.19
acculturation scale for southeast asians 1 0.19
acculturation scale for vietnamese adolescents (asva) 1 0.19
acculturation scale for vietnamese adolescents (asva); multigroup ethnic identity measure 1 0.19
acculturation t o american core culture 1 0.19
acculturative dissonance (ad) 1 0.19
acculturative stress, subjective well-being, flourishing 1 0.19
adapted acculturation rating scale for mexican-americans-ii (arsma-ii) (schaefer, et al., 2009) 1 0.19
adapted acculturation scale for vietnamese adolescents (asva) 1 0.19
adapted biculturalism scale (nagaraj, vyas, mcdonnell, dipietro) 1 0.19
adapted cultual adaptation korean elderly 1 0.19
adapted ethnic/cultural identity dimensions scale (eid scale) 1 0.19
adapted general ethnicity questionnaire 1 0.19
adapted hazuda scale 1 0.19
adapted measurement of acculturation strategies for people of african descent (maspad) - uk 1 0.19
adapted mexican american cultural values scale (macvs) 1 0.19
adapted short acculturation scale for hispanics (marin, sabogal, marin, otero-sabogal, & perez-stable, 1987) 1 0.19
adapted soviet acculturation measure (sam-ii) 1 0.19
adapted suinn-lew asian self-identity accultururation scale (sl-asia) 1 0.19
adjustment experience [own] 1 0.19
adjustments to u.s. national culture 1 0.19
adult acculturation and resiliency scale 1 0.19
affective adjustment 1 0.19
african american acculturation scale revised (klonoff & landrine, 2000) 1 0.19
american and puerto rican cultural involvement scales 1 0.19
anticipated socio-cultural adaptation scale 1 0.19
asian indian dietary acculturation measure (aidam) 1 0.19
assimilation attitude [own] 1 0.19
assimilation to french culture 1 0.19
attitudes toward homosexuality (ess) 1 0.19
attitudes towards majority culture, identification 1 0.19
attitudes towards minority culture maintenance and inter-ethnic contact scale 1 0.19
background information, heritage culture maintenance, mobility strategies and personal ability, perceived justice, self-perceptions, community contacts 1 0.19
banal integration [own] 1 0.19
behavior subscale from language, identity, and behavioral acculturation scale 1 0.19
behavioral and psychological acculturation 1 0.19
belonging, cultural memory (identity and difference cultural), empathy and reciprocity, perfection of speech, adaption 1 0.19
bicultural identification 1 0.19
bicultural involvement and adjustment scale 1 0.19
bicultural involvement questionnaire; acculturation rating scale for mexican-americans - revised (arsma-ii) 1 0.19
bicultural stress scale 1 0.19
bicultural youth acculturation questionnaire 1 0.19
bidimensional acculturation scale for marriage-based immigrants (basmbi) 1 0.19
bidimensional model of acculturation for korean american older adults 1 0.19
brief acculturation rating scale for mexican americans 1 0.19
brief sense of community scale (bscs-8); indicators of acculturation domains (raem) 1 0.19
brief symptoms inventory–18 (bsi–18) 1 0.19
civic involvement, donation and observance of cultural practices scale 1 0.19
coging strategies, contacts, friendship, belonging 1 0.19
contentment and gratitude, a strong family concept, the concept of face, an urge for knowledge, a sense of crisis, faith in life, and the identity of a marginal man [own] 1 0.19
cultural adaptation (jeong, & kim, 2016) 1 0.19
cultural adherence 1 0.19
cultural familiarity [own] 1 0.19
cultural identification 1 0.19
cultural identification [own], adapted vancouver index of acculturation (via; ryder et al., 2000) 1 0.19
cultural identity, feeling at home, well-being, perception of host society’s attitudes and policies toward them 1 0.19
cultural life style inventory 1 0.19
cultural life styles inventory 1 0.19
cultural maintenance and adoptation 1 0.19
cultural orientation, interaction 1 0.19
cultural practice, and social networks 1 0.19
culture-transmission motive; cultural identity and biculturality 1 0.19
degree of difficulty in dealing with practical, social, and interpersonal communication problems 1 0.19
demands of immigration scale 1 0.19
dietary acculturation [own] 1 0.19
dutch psychological acculturation scale, language use [own] 1 0.19
economic integration, selective cultural assimilation 1 0.19
economic success, psychological well-being (sense of belonging, life satisfaction) 1 0.19
educational achievement [own], job prestige [own], income [own], generation [own] 1 0.19
emotional acculturation, modified vancouver index of acculturation 1 0.19
emotional fit with the typical majority patterns of emotion [own] 1 0.19
emotional patterns questionnaire 1 0.19
employed [own] 1 0.19
employment status [own], diverse friends network outside ethnic community [own], association membership [own], ability to overcome difficulties [own], desire to remain [own] 1 0.19
ethnic attachment questionnaire 1 0.19
ethnic identification, language proficiency 1 0.19
ethnic identity [own] 1 0.19
ethnic identity questionnaire 1 0.19
ethnic label [own] 1 0.19
ethnic/cultural identity salience questionnaire 1 0.19
ethno-cultural identity conflict scale 1 0.19
ethnocultural identity behavior index 1 0.19
european-american values scale for asian-americans—revised (eavs-aa-r); asian-american values scale—revised (aavs-r) 1 0.19
expatriate acculturation index scale 1 0.19
expatriate cross-cultural adjustment scale 1 0.19
food similarity index (fsi) 1 0.19
food similarity index (fsi), mexican food [own] 1 0.19
frankfurt acculturation scale 1 0.19
frankfurt acculturation scale (frakk) 1 0.19
general acculturation index (gai) 1 0.19
geriatric depression scale - 30 1 0.19
ghanaian acculturation survey (gas) 1 0.19
hazuda scale [language use only] 1 0.19
host communication competence, host interpersonal communication, psychological health, functional fitness 1 0.19
host community acculturation scale & immigrant acculturation scale 1 0.19
identification; identity; behavior; islamic beliefs; social network; cultural norms; language use, choice, and preference; attiudes towards turkish language 1 0.19
identitiy [own] 1 0.19
identity style inventory cultural transition 1 0.19
identity style inventory cultural transition + immigrant identity commitment scale 1 0.19
immigrant identity questionnaire (iiq) 1 0.19
immigration policy lab (ipl) integration index 1 0.19
indonesian acculturation rating scales (iars, modified from arsma-ii) 1 0.19
integration effort 1 0.19
integration well-being scale (amit, litwin) 1 0.19
intention to return home, agreement with native values & political identification 1 0.19
interactions [own] 1 0.19
intercultural adjustment potential scale (icaps) 1 0.19
intercultural strategies (assimilation, integration) 1 0.19
interethnic contact 1 0.19
interview language 1 0.19
inventory of student adjustment, sociocultural adjustment scale, australian cultural knowledge (ack) questionnaire, cultural identiy subscale of collective self-esteem scale 1 0.19
israeli religio-ethnic acculturation 1 0.19
italian-canadian acculturation scale (kim, laroche, & tomiuk, 2001) 1 0.19
japanese american acculturation scale 1 0.19
korean acculturation scale 1 0.19
korean version of the acculturation rating scale for mexican americans-ii 1 0.19
language 1 0.19
language ability [own] 1 0.19
language acculturation [own] 1 0.19
language acquisition, dual national identity, realised expectations 1 0.19
language barriers, and getting used to life in australia 1 0.19
language difficulties, social support, economic situation, prejudice 1 0.19
language fluency [own] 1 0.19
language fluency, prior employment 1 0.19
language proficiency (icsey); cultural identity (icsey); acculturation behaviors - peer contacts & activities (icsey) 1 0.19
language proficiency [own], ethnic identification 1 0.19
language proficiency [own], identification [own], acculturation desire [own], marriage values [own], children ideals [own] 1 0.19
language proficiency [own], language use [own], media consumption [own] 1 0.19
language proficiency [own], time of residence [own], media consumption [own], national visitors [own] 1 0.19
language proficiency and length of residence 1 0.19
language proficiency, and cultural identity 1 0.19
language proficiency, language use, preferred language, food preferences, ethnicity of social contacts 1 0.19
language proficiency, languages use, preferred languages for media, food preference, ethnicity of close friends 1 0.19
language proficiency, length of residency, utilization of societal resources etc. 1 0.19
language proficiency, personal preference, social affiliation, lifestyle, and attachment to values and traditions 1 0.19
language proficiency, social support, cultural distance, contact 1 0.19
language skills [own], work status [own], ethnic identity [own] 1 0.19
language use 1 0.19
language use [own], community event participation [own] 1 0.19
language use [own], language preference media consumption [own] 1 0.19
language use, language proficiency 1 0.19
language use, length of stay, friendship composition, generation, percentage of native-born persons in the respondent’s neighborhood, and percentage of non-coethnics in the respondent’s neighborhood [own] 1 0.19
language use, media behavior, social customs, social contacts, cultural identity, generational status 1 0.19
language, identity and behavioral acculturation scale 1 0.19
language, number of years in new country, identity 1 0.19
lanuage preference [own] 1 0.19
learn the values, behaviors, life styles, and language of the host culture 1 0.19
learning language, navigation, regulations 1 0.19
leisure participation 1 0.19
level of acculturation to korean life 1 0.19
life satisfaction index a 1 0.19
locus of control, giving and receiving, individualism-collectivism, 1 0.19
mace’s (2004) acculturation index 1 0.19
male arab acculturation scale (maas) 1 0.19
measure of identification with groups 1 0.19
mental health continuum short form (mhc-sf) 1 0.19
migrant integration indexes (lindstorm, 2019) 1 0.19
minority and majority group contact (sim) 1 0.19
modified acculturation strategies and attitudes questionnaire, immigrants’ version 1 0.19
modified american and puerto rican cultural involvement scales 1 0.19
modified bicultural involvement questionnaire 1 0.19
modified global acculturation index 1 0.19
modified lowlands acculturation scale 1 0.19
modified suinn-lew asian self-identity acculturation scale 1 0.19
modified suinn-lew asian self-identity accultururation scale (sl-asia) 1 0.19
motivation for cultural maintenance (mcm); motivation for cultural exploration (mce) 1 0.19
multicultural acculturation questionnaire 1 0.19
multicultural ideology scale 1 0.19
multidimensional acculturation 1 0.19
multidimensional acculturative stress inventory 1 0.19
multidimensional measure of cultural identity scales for latinos and latinas 1 0.19
multidimensional measure of cultural identity scales for latinos and latinas [language preference, language proficiency]; ethnic identity [own] 1 0.19
multigroup ethnic identity measure; acculturation attitudes (arends-tóth & van de vijver, 2007) 1 0.19
multigroup ethnic identity measure–revised 1 0.19
mutual intercultural relations in plural societies - acculturation attitudes scale; sociocultural adaptation scale 1 0.19
national identification [own] 1 0.19
national identity 1 0.19
number of initial pleasant experiences, percentage of initial experiences which were unpleasant, number of’ problems encountered upon arrival, satisfaction with friends, satisfaction with job, satisfaction with school, satisfaction with recreation, task competence, interpersonal comfort, recent symptoms, chronic symptoms, cultural competence (english vocabulary, number series, knowledge of australian slang, knowledge of australian culture - geography, history, politics, and celebrities) 1 0.19
pan-acculturation scale (soriano, 1999) 1 0.19
participation and identification 1 0.19
perez ambiguous loss of homeland scale (palh) 1 0.19
phenomenon that occurs in multilingual and multicultural contexts over a period of time, causing alterations in the cultures involved in the process or creating a new “third” culture 1 0.19
physical activity and sport acculturation index (pasai) 1 0.19
place attachment scale (williams, & vaske, 2003); sociocultural adaptation scale 1 0.19
pooyan behavioral scale 1 0.19
preferred and perceived acculturation strategies 1 0.19
primary language [own], cultural fluency [own], cultural orientation [own] 1 0.19
proportion of native friends, feelings of inclusion, satisfaction with employment, accommodation, and life 1 0.19
psychological acculturation scale 1 0.19
psychological acculturation scale, massachusetts hispanic elderly study acculturation scale 1 0.19
psychological acculturation scale; language use [own] 1 0.19
psychological adaptation, socio-cultural adjustment scale, economic adaptation 1 0.19
psychological well-being questionnaire, emotional state questionnaire, satisfaction level questionnaire, 90-symptom checklist 1 0.19
reactions to assimilation expectations 1 0.19
revised haitian acculturation scale 1 0.19
satisfaction with migration life scale 1 0.19
self-administered questionnaire of acculturation 1 0.19
self-esteem, life satisfaction, subjective happiness, depression (reversed), anxiety (reversed), loneliness (reversed) 1 0.19
self-reported life satisfaction 1 0.19
sense of belinging [own], language use [own], social network [own] 1 0.19
short acculturation scale (marin, sabogal, marin, otero-sabogal, & perez-stable, 1987) 1 0.19
short acculturation scale for filipino americans 1 0.19
short acculturation scale for hispanics (5 item version) 1 0.19
short acculturation scale for hispanics (language subscale) 1 0.19
short acculturation scale for hispanics-youth (sash-y) 1 0.19
social adaptation structured interview, social adjustment questionnaire 1 0.19
social contacts [own] 1 0.19
social integration 1 0.19
social integration in the community scale (from pcsq) 1 0.19
social integration index (dalgard, & thapa, 2007) 1 0.19
social markers of acculturation 1 0.19
social participation [language use - own]; vancouver index of acculturation (via; ryder et al., 2000) 1 0.19
social support list - interactions scale 1 0.19
social, attitudinal, familial, and environmental (safe) acculturative stress scale 1 0.19
socio-cultural adaptation scale 1 0.19
socio-cultural adaptation scale (29-item) 1 0.19
socio-cultural well-being [own]; reading behavior [own] 1 0.19
sociocultural adaptation - school adjustment (icsey); psychological adaptation - life satisfaction (icsey); psychological adaptation - self esteem (icsey); psychological adaptation - psychological problems (icsey); sociocultural adaptation - behavior problems (icsey) 1 0.19
sociocultural adaptation scale (lsype) 1 0.19
sociocultural adaptation scale; adaptation scale (gao, & gudykunst, 1990) 1 0.19
sociocultural difficulties (unclear origin) 1 0.19
spheres of sociocultural adjustment 1 0.19
strategies of acculturation scale 1 0.19
structured interview 1 0.19
survey language [own] 1 0.19
symptom checklist-90-revised scale, satisfaction with live scale, riverside acculturation stress inventory, bicultural identity integration scale 1 0.19
teacher’s report of academic average, fluency and achievement in local language, participation and achievment in sports and recreation, interaction with peers, popularity among peers, class participation 1 0.19
the saudi - canada adaptation questionnaire (scao) 1 0.19
time in country, maintanance hertiage culture, adaptation host culture, social support migrant community, social support host community 1 0.19
time in country, value host culture, value heritage culture 1 0.19
trinity acculturation scale 1 0.19
trust [own], civic engagment measure 1 0.19
van manen’s approach to phenomenology 1 0.19
vancouver index of acculturation (via; ryder et al., 2000); self-esteem, subjective well-being; life satisfaction 1 0.19
vancouver index of acculturation - arab version 1 0.19
virgin island acculturation scale (tull, ambrose, & chambers, 2003) 1 0.19
working hours [own] 1 0.19

Experience

names <- c("Affect", "Behavior", "Cognition", "Desire")
kEmp <- dt.Empirical.included %>%
  select(Affect, Affect2, Behavior, Behavior2, Cognition, Cognition2, Desire, Desire2) %>%
  mutate_all(~replace_na(., 0)) %>%
  kappa.full.multiple(., names)
kEmpPooled <- kappa.pooled2(kEmp)

We then look at the use of experience aspects within the empirical works in more detail. All inter-rater agreements were 97.91% or above and all \(\kappa\)s were above 0.93 (\(\kappa_{pooled}\) = 0.97, \(95\%CI_{boot}\)[0.95, 0.99]; for full inter-rater reliability see Table 33).

kEmp %>%
  kbl(., 
      #label = "",
      caption = "Empirical Literature: <br>Cohen's $\\kappa$",
      format = "html", 
      #linesep = "",
      #booktabs = T,
      align = c('l', rep('c', length(.)-1)),
      digits=3)  %>%
  kableExtra::footnote(general = paste0("$\\kappa_{pooled}$ = ",format(round(kEmpPooled$k.pooled, 2), nsmall=2), ", $95\\%CI_{boot}$[", format(round(kEmpPooled$lwr, 2), nsmall=2), ", ", format(round(kEmpPooled$upr, 2), nsmall=2), "]")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 33: Empirical Literature:
Cohen’s \(\kappa\)
name Po Pe k se lwr upr
Affect 0.992 0.500 0.985 0.008 0.970 1.000
Behavior 0.979 0.684 0.934 0.020 0.895 0.973
Cognition 0.996 0.702 0.987 0.009 0.970 1.005
Desire 0.985 0.702 0.949 0.018 0.914 0.984
Note:
\(\kappa_{pooled}\) = 0.97, \(95\%CI_{boot}\)[0.95, 0.99]

We were particularly interested in the overall use of each experience aspect (see Table 34 and Figure 3), the combined uses of experience aspects (see Table 35 and Figure 21), the resulting distribution of the number of aspects considered (see Table 36 and Figure 22), as well as the number of other aspects that were considered with each of the aspects (see Table 37).

# Count the times each dimension is measured
empiricalElementFreq <- dt.Empirical.included %>%
  dplyr::select(Affect=AffectFinal, Behavior=BehaviorFinal, Cognition=CognitionFinal, Desire=DesireFinal) %>%
  mutate_at(vars(Affect, Behavior, Cognition, Desire), ~replace_na(., 0)) %>%
  colSums(., na.rm = FALSE, dims = 1) 

# transform to data frame and make row names name variable
empiricalElementFreq <- data.frame(Element = names(empiricalElementFreq), 
                            Frequency = empiricalElementFreq, 
                            Percentage = empiricalElementFreq/nrow(dt.Empirical.included)*100) %>%
  mutate(Element = fct_reorder(Element, Frequency)) %>%
  arrange(desc(Frequency))

empiricalElementFreq %>%
  kbl(., 
        #label = "",
        caption = "Empirical Literature: <br>Overall Aspect Frequency",
        format = "html", 
        #linesep = "",
        #booktabs = T,
        align = c('l', 'c', 'c'))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 34: Empirical Literature:
Overall Aspect Frequency
Element Frequency Percentage
Cognition Cognition 430 81.75
Behavior Behavior 422 80.23
Affect Affect 258 49.05
Desire Desire 98 18.63 
# barplot of dimension frequency
empiricalABCDBar <- ggplot(data=empiricalElementFreq, aes(x=Element, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    #aes(label = paste0("N = ",Frequency)),
    aes(label = paste0(format(round(Percentage,2), nsmall=2), "%")),
    position=position_stack(vjust=0.5),
    color = "white",
    size = 4,
    vjust = 0.5
    ) +
  labs(#title = "Aspect Frequency",
       y = "Percentage across all Studies",
       x = "Experience Aspect")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size=10, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=10),
        axis.text.y = element_text(size=10),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        legend.position="none")

empiricalABCDBar
Empirical Literature: Bar Graph Aspect Frequency

Figure 20: Empirical Literature: Bar Graph Aspect Frequency 

# frequency of unique combinations
empiricalElementCombFreq <- dt.Empirical.included %>%
  dplyr::select(Affect=AffectFinal, Behavior=BehaviorFinal, Cognition=CognitionFinal, Desire=DesireFinal) %>%
  #mutate_at(vars(Affect, Behavior, Cognition, Desire), ~replace_na(., 0)) %>%
  group_by(Affect, Behavior, Cognition, Desire) %>%
  summarise(Frequency = n()) %>%
  ungroup() %>%
  mutate(complexity = rowSums(dplyr::select(., Affect, Behavior, Cognition, Desire), na.rm = T))

# fill replace ones with colnames to be combined
for (i in 1:4) {
    empiricalElementCombFreq[[i]] <- str_replace(as.character(empiricalElementCombFreq[[i]]), "1", colnames(empiricalElementCombFreq)[i])
}

# collect Elements names for each combination
empiricalElementCombFreq <- empiricalElementCombFreq %>%
  unite("ExperienceCombination",c("Affect", "Behavior", "Cognition", "Desire"), na.rm = TRUE, sep = ", ") %>%
  mutate(ExperienceCombination = fct_reorder(ExperienceCombination, Frequency),
         Percentage = Frequency/nrow(dt.Empirical.included)*100,
         Affect = ifelse(grepl("Affect", ExperienceCombination, fixed = TRUE), 1,0),
         Behavior = ifelse(grepl("Behavior", ExperienceCombination, fixed = TRUE), 1,0),
         Cognition = ifelse(grepl("Cognition", ExperienceCombination, fixed = TRUE), 1,0),
         Desire = ifelse(grepl("Desire", ExperienceCombination, fixed = TRUE), 1,0)) %>%
  arrange(-Frequency)

empiricalElementCombFreq %>%
  kbl(., 
      #label = "",
      caption = "Empirical Literature: Aspect Combinations",
      format = "html", 
      #linesep = "",
      #booktabs = T,
      digits = 2,
      align = c('l', rep('c', length(empiricalElementCombFreq)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 35: Empirical Literature: Aspect Combinations
ExperienceCombination Frequency complexity Percentage Affect Behavior Cognition Desire
Affect, Behavior, Cognition 158 3 30.04 1 1 1 0
Behavior, Cognition 107 2 20.34 0 1 1 0
Behavior 73 1 13.88 0 1 0 0
Cognition 47 1 8.94 0 0 1 0
Affect, Behavior, Cognition, Desire 46 4 8.75 1 1 1 1
Affect, Cognition 31 2 5.89 1 0 1 0
Behavior, Cognition, Desire 26 3 4.94 0 1 1 1
Cognition, Desire 10 2 1.90 0 0 1 1
Affect, Behavior 6 2 1.14 1 1 0 0
Affect 6 1 1.14 1 0 0 0
Affect, Cognition, Desire 5 3 0.95 1 0 1 1
Affect, Desire 5 2 0.95 1 0 0 1
Behavior, Desire 5 2 0.95 0 1 0 1
Affect, Behavior, Desire 1 3 0.19 1 1 0 1 
# bar plot frequencies
empiricalABCDComb <- ggplot(empiricalElementCombFreq, aes(x=ExperienceCombination, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    aes(y=Percentage, label = paste0(format(round(Percentage,2), nsmall=2),"% [N = ", Frequency, "]")),
    color = "grey14",
    size = 4,
    hjust = -.1,
    inherit.aes = TRUE
    ) +
  scale_y_continuous(limits = c(0, ceiling(max(empiricalElementCombFreq$Percentage)*1.15)),
                     breaks = seq(0, ceiling(max(empiricalElementCombFreq$Percentage)*1.15), 5))+
  labs(y = "Proportion of all Studies [in %]",
       x = "Combination of Experience Aspects")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")

empiricalABCDComb
Empirical Literature: Bar Graph Aspect Combinations

Figure 21: Empirical Literature: Bar Graph Aspect Combinations 

# summarize by aspect complexity
empiricalComplexity <- empiricalElementCombFreq %>%
  dplyr::select(complexity, Frequency) %>%
  group_by(complexity) %>%
  summarise(Frequency = sum(Frequency),
            Percentage = sum(Frequency)/nrow(dt.Empirical.included)*100) %>%
  ungroup() %>%
  mutate(complexity = as.factor(complexity),
         complexity = fct_reorder(complexity, Frequency)) %>%
  arrange(desc(Frequency))

# overall complexity mean and standard deviation
empiricalComplexityAverage <- weighted.mean(as.numeric(as.character(empiricalComplexity$complexity)), empiricalComplexity$Frequency)
empiricalComplexitySD <- wtd.var(x = as.numeric(as.character(empiricalComplexity$complexity)), weights = empiricalComplexity$Frequency)

# Table complexity distribution
empiricalComplexity %>%
  kbl(., 
        #label = "",
        caption = "Empirical Literature: <br>Number of Aspects considered",
        format = "html", 
        digits = 2,
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(empiricalComplexity)-1)))  %>%
  kableExtra::footnote(general = paste0("M = ",format(round(empiricalComplexityAverage, 2), nsmall=2), ", SD = ", format(round(empiricalComplexitySD, 2), nsmall=2))) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 36: Empirical Literature:
Number of Aspects considered
complexity Frequency Percentage
3 190 36.12
2 164 31.18
1 126 23.95
4 46 8.75
Note:
M = 2.30, SD = 0.86 
# barplot of complexity frequency
empiricalComplexityBar <- ggplot(data=empiricalComplexity, aes(x=complexity, y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    #aes(label = paste0("N = ",Frequency)),
    aes(label = paste0(format(round(Percentage,2), nsmall=2), "%")),
    position=position_stack(vjust=0.5),
    color = "white",
    size = 4,
    vjust = 0.5
    ) +
  labs(title = "Empirical Literature: Numer of Aspects Considered",
       y = "Frequency across all Studies",
       x = "Number of Aspects Considered")+
  coord_flip()+
  theme_Publication()+
  theme(strip.background = element_rect(fill="grey14", color="grey14", ),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(size=10, face="bold", hjust = 0.5),
        axis.text.x = element_text(size=10),
        axis.text.y = element_text(size=10),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        strip.text = element_text(colour = 'white', face="bold"),
        panel.background = element_rect(fill = "transparent"),
        plot.background = element_rect(fill = alpha('white', 0.5)), 
        legend.position="none")

empiricalComplexityBar
Empirical Literature: Bar Graph Number of Aspects considered

Figure 22: Empirical Literature: Bar Graph Number of Aspects considered 

# Numer of Aspects Considered for each element
empiricalElementComplexity <- empiricalElementCombFreq %>%
  gather(key = "Element", value = "ElementDum", Affect, Behavior, Cognition, Desire) %>%
  filter(ElementDum == 1) %>%
  group_by(Element) %>%
  summarise(n = sum(Frequency),
            avgComplexity = weighted.mean(x = complexity, w = Frequency),
            sdComplexity = wtd.var(x = complexity, weights = Frequency)) %>%
  ungroup() %>%
  mutate(seComplexity = sdComplexity/sqrt(n)) %>%
  arrange(-avgComplexity)

# Table Aspect complexity distribution
empiricalElementComplexity %>%
  kbl(., 
        #label = "",
        caption = "Methodological Literature: <br>Number of Aspects considered with each aspect",
        col.names = c("Aspect", "N", "Mean", "Standard Deviation", "Standard Error"),
        format = "html", 
        digits = 2,
        #linesep = "",
        #booktabs = T,
        align = c('l', rep('c', length(empiricalElementComplexity)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 37: Methodological Literature:
Number of Aspects considered with each aspect
Aspect N Mean Standard Deviation Standard Error
Desire 98 3.27 0.61 0.06
Affect 258 2.97 0.43 0.03
Cognition 430 2.54 0.68 0.03
Behavior 422 2.48 0.82 0.04

We additionally inspected bi-variate relations between the individual aspects. We calculate the phi coefficient (for binary variables) together with the raw number of co-occurrences (see Table 38).

# make crossproduct matrix to condense co-occurrences (off-diagonals) and get frequencies (diagonals)
as.matrix(dt.Empirical.included %>% dplyr::select(Affect=AffectFinal, Behavior=BehaviorFinal, Cognition=CognitionFinal, Desire=DesireFinal) %>%
            mutate_all(~replace(., is.na(.), 0))) %>%
  BinaryCor(., "pearson") %>%
  tibble::rownames_to_column(., var = "Aspect") %>%
  kbl(., 
        #label = "",
        caption = "Empirical Literature: <br>Aspects Bi-Variate Relations",
        format = "html", 
        linesep = "",
        booktabs = T,
        align = c('l', rep('c', ncol(.)-1)))  %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 38: Empirical Literature:
Aspects Bi-Variate Relations
Aspect Affect Behavior Cognition Desire
Affect N = 258 0.04 0.29*** 0.09*
Behavior 211 N = 422 -0.10* -0.01
Cognition 240 337 N = 430 0.09*
Desire 57 78 87 N = 98 
# draw combined graph
cowplot::ggdraw() +
  cowplot::draw_plot(empiricalABCDComb, x = 0, y = 0.3, width = 1, height = .7)+
  cowplot::draw_plot(empiricalABCDBar, x = 0, y = 0, width = .55, height = .3) +
  cowplot::draw_plot(empiricalComplexityBar, x = .55, y = 0, width = .45, height = .3) +
  cowplot::draw_plot_label(c("(A)", "(B)", "(C)"), c(0, 0, 0.55), c(1, 0.3, 0.3), size = 15)
Empirical Literature: Combined Bar Graphs

Figure 23: Empirical Literature: Combined Bar Graphs

Context

Culture

We also coded the cultural context the scales were validated in. We coded both the migrants’ country of origin as well as the country of the receivong society in which the study was conducted (see Figure 24).

# Frequencies of host country focus
empiricalHostFreq <- dt.Empirical.included %>%
  dplyr::select(HostCountry) %>%
  mutate(country = strsplit(as.character(HostCountry), ", ")) %>% 
  unnest(country) %>%
  group_by(country) %>%
  summarise(Host = n()) %>%
  arrange(-Host)
  
# Frequencies of origin country focus
empiricalOriginFreq <- dt.Empirical.included %>%
  dplyr::select(OriginCountry) %>%
  mutate(country = strsplit(as.character(OriginCountry), ", ")) %>% 
  unnest(country) %>%
  group_by(country) %>%
  summarise(Origin = n()) %>%
  arrange(-Origin)

# Frequencies of host country focus in wide format
empiricalCountryFreqWide <- merge(empiricalHostFreq, empiricalOriginFreq, by = "country", all = TRUE) %>%
  mutate_if(is.numeric, funs(replace_na(., 0)))

# Frequencies of host country focus in long format
empiricalCountryFreqLong <- empiricalCountryFreqWide %>%
  melt(., id="country", value.name = "Frequency")
# bar plot country frequencies
ggplot(empiricalCountryFreqLong , aes(x=reorder(country, Frequency), y=Frequency)) +
  geom_bar(stat="identity", fill="grey14") +
  labs(x = "Country") +
  coord_flip()+
  facet_wrap( ~ variable, nrow = 1) +
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Empirical Literature: Bar graph of study counts for the individual host countries and countries of origin.

Figure 24: Empirical Literature: Bar graph of study counts for the individual host countries and countries of origin.

Individual

We, again, coded the type of sample the original authors used in their validation studies (see Figure 25).

# tally different samples
empiricalSampleFreq <- as.data.frame(table(Sample = dt.Empirical.included$Sample)) %>%
  arrange(Freq) %>%    # First sort by val. This sort the dataframe but NOT the factor levels
  mutate(Sample=factor(Sample, levels=Sample)) # update factor levels
  
# barplot of sample frequency
ggplot(data=empiricalSampleFreq, aes(x=Sample, y=Freq)) +
  geom_bar(stat="identity", fill="grey14") +
  ylab("Frequency") +
  ggtitle("Validation Sample") +
  coord_flip()+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Bar graph of the study samples used in the empirical studies.

Figure 25: Bar graph of the study samples used in the empirical studies.

The category general refers to a sampling strategy in which any consenting adult could participate in the study.

Situation

We coded which life domains the authors referred to, either as part of subscale labels, factor labels, explicit commentary of the authors, or clear question wordings to gain an understanding of the situational focus the authors chose. However, we did not code the theoretical situational life domains because such an undertaking would be beyond the scope of this paper.

Process

To assess the temporal focus of the empirical studies, we also checked whether studies sampled migrants prior to migration, post-migration, or both (see Table 39).

# table of the migration time focus
dt.Empirical.included %>%
  dplyr::select(MigrationTime) %>%
  mutate(MigrationTime = replace_na(MigrationTime, "N/A")) %>%
  group_by(MigrationTime) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Empirical Literature: <br>Migration Time",
      format = "html",
      digits = 2,
      col.names = c("Migration Time", "Frequency", "Percentage")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 39: Empirical Literature:
Migration Time
Migration Time Frequency Percentage
post 512 97.34
pre & post 10 1.90
N/A 2 0.38
potential 2 0.38

Finally, we also coded what kind of analyses the authors conducted with the acculturation measurements in the empirical studies (see Table 40).

# tally different analysis types
dt.Empirical.included %>%
  dplyr::select(MainAnalysis) %>%
  mutate(MainAnalysis = replace_na(MainAnalysis, "N/A")) %>%
  group_by(MainAnalysis) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Empirical Literature: Main Analysis in which Acculturation Measure was used",
      format = "html",
      digits = 2,
      col.names = c("Analysis", "Frequency", "Percentage")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 40: Empirical Literature: Main Analysis in which Acculturation Measure was used
Analysis Frequency Percentage
regression (incl. PROCESS) 277 52.66
mean difference 57 10.84
SEM 47 8.94
correlation 41 7.79
validation 32 6.08
descriptive (static) 18 3.42
longitudinal (dynamic) 18 3.42
content analysis 8 1.52
N/A 7 1.33
cluster 7 1.33
lagged regression 3 0.57
lagged (dynamic) 2 0.38
participant selection 2 0.38
Social Network Analysis 1 0.19
dynamic 1 0.19
open and axial coding, and constant comparative coding 1 0.19
path analysis 1 0.19
phenomenological praxis 1 0.19
process (dynamic) 1 0.19
static 1 0.19

Additionally, we coded where in the model the acculturation measure was placed (see Table 41).

# tally different variable types
dt.Empirical.included %>%
  dplyr::select(VariableType) %>%
  mutate(VariableType = replace_na(VariableType, "N/A")) %>%
  group_by(VariableType) %>%
  summarise(Frequency = n(),
            Percentage = Frequency/nrow(.)*100) %>%
  arrange(desc(Frequency)) %>%
  kbl(., caption = "Empirical Literature: Variable Type of Acculturation Analysis",
      format = "html",
      digits = 2,
      col.names = c("Variable Type", "Frequency", "Percentage")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 41: Empirical Literature: Variable Type of Acculturation Analysis
Variable Type Frequency Percentage
Predictor 285 54.18
Dependent 148 28.14
Correlation 37 7.03
Predictor & Dependent 25 4.75
Mediator 14 2.66
N/A 7 1.33
Moderator 6 1.14
selection criterion 2 0.38
Control 1 0.19
validation; Predictor 1 0.19

Field Differences

To assess the differences between fields we merged the Scimago Journal Ranking Database with our empirical review. For all available journal articles we added information on key journal metrics:

  • Scimago Journal Rank Indicator (SJR, year-average weighted citations of articles published in past 3-year period)
  • H Index (h number of articles that have been cited at least h times)
  • Number of articles published in 2019
  • Number of articles published in 2017, 2018, 2019
  • Number of references in published articles (2019)
  • Number of citations in 2016-2018
  • Number of citable documents 2016-2018
  • Average number of citations per document in a 2-year period
  • Average number of references per document in 2019
  • Country of publisher
  • Region of publisher
  • Time period of journal activity (Coverage)
  • Keywords (Categories)
  • Field

Merging

We first merged the full Scimago Journal database with our own database of empirical studies.

# unify Journal ISSNs for data merge 
psychData <- dt.Empirical.included %>%
  mutate(ISSN = gsub("-", "", ISSN))
PublisherInfo <- PublisherInfo %>%
  separate(Issn, c("Issn01", "Issn02"), remove = FALSE)

# Merge Scimago database with empirical studies
psychData <- sqldf("SELECT l.*, r.*
              FROM psychData as l
              LEFT JOIN PublisherInfo as r
              on l.PublicationTitle = r.PublicationTitleDb OR l.ISSN = r.Issn01 OR l.ISSN = r.Issn02")

# filter studies that are not in the Scimago database
psychDataJournal <- psychData %>% filter(!is.na(PublicationTitleDb))

Note that dissertations, book chapters, and books were excluded from this analysis because data on their publishers is not readily available or unreliable. Additionally, 19 journals were not included in the Scimago database (likely because they do not have an ISSN identifier or were discontinued before 1996; see Table 42 for the missing Journals).

# List Journals not in Scimago database
psychData %>% 
  filter(PublicationType == "journalArticle", 
         is.na(PublicationTitleDb)) %>%
  dplyr::select(`Journal Name` = PublicationTitle) %>%
  unique() %>%
  kbl(., caption = "Journal Information Missing in Scimago Journal Ranking",
      format = "html", row.names = FALSE) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 42: Journal Information Missing in Scimago Journal Ranking
Journal Name
Canadian Journal of Counselling and Psychotherapy
Psychology in Russia: State of the Art
Psychology and Developing Societies
Psychology: The Journal of the Hellenic Psychological Society
Hispanic Health Care International
Journal of Immigrant Health
Journal of Mental Health Counseling
Arab Journal of Psychiatry
The Irish Journal of Psychology
International Review of Applied Psychology
Journal of Nutrition Education
Current Research in Social Psychology
Archives of General Psychiatry
Universitas Psychologica
Psychological Monographs: General and Applied
Psicologia Sociale
Zeitschrift für Psychologie
Psychiatriki
International Journal of Culture and Mental Health

This meant that we ultimately had journal metrics for 425 empirical articles. The frequencies of the journals, and a selection of their impact metrics is shown in Table 43.

# {r JournalFreqTab, results='asis', warning=F, message=F}
# table with basic frequencies and journal descriptives
psychDataJournal %>%
  dplyr::select(PublicationTitleDb, SJR, Hindex, TotalDocs3years, CitesDoc2years, RefDoc, PublisherCountry) %>%
  group_by(PublicationTitleDb, SJR, Hindex, TotalDocs3years, CitesDoc2years, RefDoc, PublisherCountry) %>%
  summarise(Frequency = n()) %>%
  arrange(desc(Frequency)) %>%
  dplyr::select(Journal = PublicationTitleDb, N = Frequency, SJR, CitesDoc2years, Hindex, RefDoc, TotalDocs3years, PublisherCountry) %>%
  kbl(., caption = "Journal Frequency",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
  kableExtra::footnote(general = "SJR = Scimago Journal Rank Indicator; 
           CitesDoc2years = Average number of citations per document in a 2-year period; 
           Hindex = H Index (h number of articles that have been cited at least h times);
           RefDoc = Average number of references per document in 2019;
           TotalDocs3years = Number of articles published in 2017, 2018, 2019") %>%
   scroll_box(height = "500px")
Table 43: Journal Frequency
Journal N SJR CitesDoc2years Hindex RefDoc TotalDocs3years PublisherCountry
Journal of Immigrant and Minority Health 53 0.718 1.64 51 39.69 554 United States
International Journal of Intercultural Relations 43 0.721 2.39 75 66.32 163 United Kingdom
Journal of Cross-Cultural Psychology 14 1.086 2.45 106 60.28 272 United States
Cultural Diversity and Ethnic Minority Psychology 11 1.343 2.72 74 26.07 177 United States
International Journal of Psychology 10 0.651 1.61 58 29.74 212 United States
International Journal of Social Psychiatry 8 0.676 1.73 58 43.21 291 United Kingdom
Journal of Community Psychology 6 0.462 1.43 82 57.08 222 United States
Journal of Transcultural Nursing 5 0.578 1.53 46 33.82 248 United States
American Journal of Community Psychology 4 0.829 2.33 109 59.36 263 United States
American Journal of Orthopsychiatry 4 0.930 2.41 91 37.70 226 United States
European Journal of Social Psychology 4 1.401 2.96 106 70.72 240 United Kingdom
Journal of Applied Social Psychology 4 0.835 1.89 104 61.00 181 United States
Journal of Ethnic and Cultural Diversity in Social Work 4 0.553 2.04 27 46.55 92 United States
Journal of Social Psychology 4 0.555 1.84 72 47.68 173 United States
Social Science Research 4 1.180 2.32 82 71.58 368 United States
Social Science and Medicine 4 1.944 4.34 229 60.01 1594 United Kingdom
Transcultural Psychiatry 4 0.630 2.03 52 56.51 126 United Kingdom
Aging and Mental Health 3 1.053 2.86 82 48.50 549 United Kingdom
Australian Psychologist 3 0.573 1.67 44 51.70 169 United States
Child Development 3 2.880 5.61 243 60.17 515 United States
Ethnicity and Health 3 0.688 2.85 54 46.01 232 United Kingdom
European Psychologist 3 1.224 3.29 52 67.11 75 United States
Frontiers in Psychology 3 0.914 2.54 95 59.14 6884 Switzerland
International Journal of Behavioral Development 3 1.159 2.21 86 47.68 205 United States
Issues in Mental Health Nursing 3 0.396 1.12 57 33.00 458 United Kingdom
Journal of Community and Applied Social Psychology 3 0.687 2.26 60 47.27 121 United Kingdom
Journal of Cross-Cultural Gerontology 3 0.505 1.28 38 53.74 82 United States
Journal of Health and Social Behavior 3 1.314 2.29 122 50.69 118 United States
Journal of Human Behavior in the Social Environment 3 0.300 0.81 30 41.25 213 United States
Journal of Pacific Rim Psychology 3 0.283 0.86 12 56.94 55 United Kingdom
Nicotine and Tobacco Research 3 1.625 3.43 108 30.88 835 United Kingdom
PLoS ONE 3 1.023 2.94 300 45.68 63029 United States
Psychological Reports 3 0.469 1.75 63 52.30 238 United States
Social Indicators Research 3 0.875 2.21 99 58.08 986 Netherlands
AIDS and Behavior 2 1.783 3.55 99 46.94 1100 Netherlands
American Journal of Health Promotion 2 0.838 2.11 87 26.29 400 United States
American Journal of Public Health 2 2.210 4.20 253 14.98 1816 United States
Annals of the American Academy of Political and Social Science 2 0.977 2.25 94 49.68 267 United States
Applied Research in Quality of Life 2 0.527 2.13 28 60.54 204 Netherlands
Asian American Journal of Psychology 2 0.621 1.21 26 43.97 91 United States
Asian Journal of Social Psychology 2 0.445 1.20 47 45.74 102 United Kingdom
BMC Psychiatry 2 1.341 3.15 88 47.98 1269 United Kingdom
Basic and Applied Social Psychology 2 0.591 1.83 64 60.84 102 United States
Canadian Journal of Behavioural Science 2 0.461 1.04 50 24.84 86 United States
Child and Adolescent Social Work Journal 2 0.588 1.41 40 53.56 148 United States
Cross-Cultural Research 2 0.561 2.26 38 64.75 62 United States
Developmental Psychology 2 2.258 3.81 203 43.41 557 United States
Ethnic and Racial Studies 2 1.034 1.88 87 43.93 539 United Kingdom
General Hospital Psychiatry 2 1.430 3.08 100 45.33 292 Netherlands
Health Care for Women International 2 0.442 1.08 49 40.55 297 United Kingdom
Hispanic Journal of Behavioral Sciences 2 0.438 1.00 59 35.81 88 United States
International Journal of Aging and Human Development 2 0.504 1.52 55 45.75 126 United States
International Journal of Public Opinion Research 2 1.852 2.96 52 48.25 82 United Kingdom
Journal of Adolescence 2 1.186 3.16 109 55.04 447 United States
Journal of Aging and Health 2 1.062 2.36 71 49.83 232 United States
Journal of Behavioral Health Services and Research 2 0.615 1.68 52 44.25 164 United States
Journal of Child and Family Studies 2 0.793 1.72 70 58.46 1001 United States
Journal of Interpersonal Violence 2 0.938 5.38 99 51.87 1219 United States
Journal of Psychology: Interdisciplinary and Applied 2 0.660 2.05 58 58.41 140 United States
Journal of Psychosomatic Research 2 1.053 3.09 148 44.55 514 United States
Journal of Youth Studies 2 0.928 2.32 50 57.03 254 United Kingdom
Journal of Youth and Adolescence 2 1.773 3.83 110 57.77 495 Netherlands
Management Communication Quarterly 2 0.784 1.95 60 51.13 91 United States
Mental Health, Religion and Culture 2 0.284 0.53 40 47.01 255 United Kingdom
Personality and Social Psychology Bulletin 2 2.082 3.68 185 60.51 355 United States
Preventive Medicine 2 1.744 4.01 164 39.49 1062 United States
Social Behavior and Personality 2 0.357 0.85 53 35.72 473 New Zealand
Social Psychiatry and Psychiatric Epidemiology 2 1.775 4.23 116 47.94 461 Germany
Substance Use and Misuse 2 0.671 1.71 76 48.52 680 United Kingdom
Academic Psychiatry 1 0.566 1.45 40 16.14 594 United States
Acta Psychiatrica Scandinavica 1 2.516 5.95 140 48.82 420 United Kingdom
Adult Education Quarterly 1 0.732 1.90 45 53.04 49 United Kingdom
Alcohol and Alcoholism 1 0.793 2.24 94 30.55 316 United Kingdom
American Behavioral Scientist 1 0.781 2.05 102 48.13 336 United States
American Journal of Men’s Health 1 0.689 2.13 31 40.90 467 United States
American Journal of Psychiatry 1 6.071 11.87 344 25.51 721 United States
Appetite 1 1.341 3.99 133 54.04 1334 Netherlands
Applied Developmental Science 1 0.876 3.00 69 69.03 82 United States
Archives of Psychiatric Nursing 1 0.495 1.48 47 42.60 380 United Kingdom
Asia-Pacific Journal of Public Health 1 0.551 1.27 33 25.88 281 United States
Assessment 1 2.040 4.09 80 61.32 253 United States
British Journal of Guidance and Counselling 1 0.287 1.03 43 49.99 177 United States
British Journal of Social Psychology 1 1.501 3.20 93 63.44 139 United States
Canadian Journal of Psychiatry 1 1.966 3.71 109 40.06 327 Canada
Canadian Journal of School Psychology 1 0.283 0.91 27 48.50 62 United Kingdom
Canadian Journal on Aging 1 0.506 1.54 42 43.94 140 Canada
Career Development International 1 1.052 3.49 56 75.17 119 United Kingdom
Clinical Gerontologist 1 0.551 2.04 30 40.88 141 United States
Clinical Practice and Epidemiology in Mental Health 1 0.442 1.65 45 38.36 79 United Arab Emirates
Communication Monographs 1 1.979 5.57 66 59.17 81 United Kingdom
Contemporary Drug Problems 1 0.674 1.95 11 68.33 70 United States
Counseling Psychologist 1 1.055 1.96 74 75.35 142 United States
Cross Cultural and Strategic Management 1 0.825 2.63 37 105.55 101 United Kingdom
Culture, Health and Sexuality 1 0.952 2.52 60 42.51 307 United Kingdom
Culture, Medicine and Psychiatry 1 0.629 1.55 54 62.09 117 United States
Current Opinion in Psychology 1 1.978 5.05 33 48.91 486 United Kingdom
Current Psychology 1 0.506 3.04 39 57.06 569 United States
Cyberpsychology, Behavior, and Social Networking 1 1.352 3.18 133 27.32 407 United States
Development and Psychopathology 1 1.739 3.92 164 83.16 384 United Kingdom
Drug and Alcohol Dependence 1 1.909 4.22 158 46.39 1356 Netherlands
Eating and Weight Disorders 1 0.728 4.07 41 44.74 273 Switzerland
European Journal of Marketing 1 1.033 2.94 91 78.44 333 United Kingdom
Human Organization 1 0.365 0.83 51 60.97 97 United States
Identity 1 0.478 1.05 29 49.05 69 United Kingdom
International Journal of Hospitality Management 1 2.217 8.43 106 76.16 373 United Kingdom
International Nursing Review 1 0.695 2.12 48 28.35 231 United Kingdom
International Review for the Sociology of Sport 1 0.860 2.65 54 61.87 164 United Kingdom
International Review of Social Psychology 1 1.103 3.45 18 43.33 48 United Kingdom
International Social Work 1 0.445 1.10 40 44.00 307 United Kingdom
Journal for the Scientific Study of Religion 1 0.898 1.15 68 58.53 162 United States
Journal of Advertising 1 3.373 9.29 101 63.31 122 United States
Journal of Affective Disorders 1 1.945 4.45 174 52.72 2359 Netherlands
Journal of Aging Studies 1 0.613 1.61 58 54.67 112 United Kingdom
Journal of Anxiety Disorders 1 2.056 3.34 107 56.73 272 United Kingdom
Journal of Behavioral Medicine 1 1.225 3.23 90 48.90 288 United States
Journal of Black Studies 1 0.349 0.65 38 50.25 125 United States
Journal of Cancer Education 1 0.564 1.65 40 26.61 469 United States
Journal of Clinical Nursing 1 0.809 2.37 96 42.34 1427 United Kingdom
Journal of Community Health 1 0.719 1.69 59 32.43 474 Netherlands
Journal of Comparative Family Studies 1 0.237 0.31 37 43.88 88 Canada
Journal of Conflict Resolution 1 3.031 3.44 101 62.19 206 United States
Journal of Consumer Marketing 1 0.751 1.99 91 65.48 179 United Kingdom
Journal of Counseling Psychology 1 2.533 4.48 126 31.38 195 United States
Journal of Early Adolescence 1 1.108 2.34 66 61.13 140 United States
Journal of Epidemiology and Community Health 1 1.710 3.89 162 36.33 541 United Kingdom
Journal of Family Psychology 1 1.376 2.41 115 31.89 329 United States
Journal of Family Violence 1 0.697 1.72 74 49.13 229 United States
Journal of Gerontological Social Work 1 0.413 1.18 40 25.37 166 United States
Journal of Happiness Studies 1 1.193 3.20 67 65.89 343 Netherlands
Journal of Human Nutrition and Dietetics 1 0.991 3.54 64 45.08 245 United Kingdom
Journal of Management and Organization 1 0.505 2.79 30 73.30 192 United Kingdom
Journal of Managerial Psychology 1 1.046 2.17 74 50.80 156 United Kingdom
Journal of Marriage and Family 1 1.671 2.66 152 58.54 257 United Kingdom
Journal of Mental Health 1 0.910 2.85 63 42.29 289 United Kingdom
Journal of Multicultural Counseling and Development 1 1.314 2.00 40 40.67 50 United States
Journal of Muslim Mental Health 1 0.376 0.19 17 46.18 31 United States
Journal of Nervous and Mental Disease 1 0.832 1.85 118 38.92 460 United States
Journal of Nursing Scholarship 1 1.074 3.04 76 35.06 236 United Kingdom
Journal of Personality 1 2.593 4.40 135 65.14 205 United Kingdom
Journal of Personality Assessment 1 1.221 2.78 95 52.57 214 United States
Journal of Personality and Social Psychology 1 4.539 7.37 351 50.77 341 United States
Journal of Primary Prevention 1 0.622 1.86 52 40.54 116 United States
Journal of Psychiatric and Mental Health Nursing 1 0.618 2.15 60 45.90 223 United Kingdom
Journal of School Psychology 1 1.789 4.13 89 77.62 146 United Kingdom
Journal of Social Service Research 1 0.296 0.90 33 47.74 162 United States
Journal of Social and Personal Relationships 1 1.099 2.80 79 55.94 173 United Kingdom
Journal of Traumatic Stress 1 0.939 2.22 130 44.67 259 United States
Journal of Vocational Behavior 1 2.210 5.40 141 81.80 242 United States
Journal of Women’s Health 1 1.042 2.29 93 34.05 582 United States
Journal of physical activity & health 1 0.777 2.05 68 39.68 543 United States
Journal of the American Association of Nurse Practitioners 1 0.473 1.42 16 13.84 332 United Kingdom
Journal of the American Psychiatric Nurses Association 1 0.481 1.66 28 30.52 143 United States
Journal of the National Medical Association 1 0.355 1.12 66 28.77 201 United States
Journals of Gerontology - Series B Psychological Sciences and Social Sciences 1 1.591 3.54 142 36.50 415 United States
Kinesiology 1 0.454 1.48 19 34.53 113 Croatia
Leisure Sciences 1 0.597 2.52 62 57.77 131 United Kingdom
Marriage and Family Review 1 0.659 1.46 37 55.60 123 United States
Mass Communication and Society 1 2.252 2.74 32 58.43 116 United States
Maternal and Child Health Journal 1 1.014 2.10 80 30.56 801 United States
Measurement and Evaluation in Counseling and Development 1 0.417 1.96 46 46.83 67 United States
Narrative Inquiry 1 0.339 1.05 41 54.22 56 Netherlands
New Zealand Journal of Psychology 1 0.263 0.48 29 41.16 59 New Zealand
Open Psychology Journal 1 0.236 0.81 9 45.70 63 Netherlands
Pediatric obesity 1 1.455 3.65 63 39.37 274 United Kingdom
Perceptual and Motor Skills 1 0.459 1.51 64 47.66 259 United States
Personality and Individual Differences 1 1.288 2.90 155 47.25 1966 United Kingdom
Prevention Science 1 1.407 3.38 81 46.86 323 Netherlands
Proceedings of the National Academy of Sciences of the United States of America 1 5.165 9.35 737 45.05 11653 United States
Psicologia Conductual 1 0.334 1.18 21 52.19 82 Spain
Psychiatry Research 1 1.019 2.49 127 50.21 2525 Ireland
Psychology and Health 1 1.007 2.96 85 57.90 269 United Kingdom
Psychology in the Schools 1 0.676 1.53 71 58.39 257 United States
Qualitative Health Research 1 1.571 3.18 106 51.45 527 United States
Reading Research Quarterly 1 2.060 4.40 86 81.84 83 United States
Research in Nursing and Health 1 0.758 2.39 81 36.38 189 United States
Risk Analysis 1 1.092 3.58 124 54.31 561 United Kingdom
Scandinavian Journal of Psychology 1 0.794 1.81 69 54.71 221 United Kingdom
Social Identities 1 0.347 1.14 33 46.59 135 United Kingdom
Social Psychology 1 1.117 2.23 33 53.12 97 Germany
Social Psychology Quarterly 1 1.106 1.57 81 56.38 61 United States
Sociological Quarterly 1 0.507 1.14 58 65.71 108 United States
South African Journal of Psychology 1 0.378 1.20 32 38.28 145 United States
Spanish Journal of Psychology 1 0.389 2.43 38 42.73 252 United Kingdom
TPM - Testing, Psychometrics, Methodology in Applied Psychology 1 0.350 1.15 17 54.26 101 Italy
The Lancet Psychiatry 1 6.967 17.42 65 17.42 995 United Kingdom
Violence Against Women 1 0.888 2.58 90 53.94 277 United States
Women’s Health Issues 1 1.251 2.51 56 40.58 293 Netherlands
Note:
SJR = Scimago Journal Rank Indicator;
CitesDoc2years = Average number of citations per document in a 2-year period;
Hindex = H Index (h number of articles that have been cited at least h times);
RefDoc = Average number of references per document in 2019;
TotalDocs3years = Number of articles published in 2017, 2018, 2019


To gain a broad understanding of the interest development we plotted the yearly average H Index (Figure 26) and yearly average of the journals’ citations per paper over time (Figure 27). It should be noted that the citation per paper metric is based in the years of 2018/19 and might not represent the citation impact of the journals at the time of the publications. Yet the metric should offer a first insight into average level of journal outlet selected by the authors. Also note that confidence bands are only calculated for a yearly N > 1 and only displayed with two or more consecutive years of data.

# summarize journal statistics by year
library(ggrepel)
hTime <- psychDataJournal %>%
  dplyr::select(year, SJR, Hindex, TotalDocs3years, CitesDoc2years, RefDoc) %>%
  mutate(across(SJR:RefDoc, ~ as.numeric(gsub("," ,".", .x)))) %>%
  group_by(year) %>%
  summarise_at(vars(c("SJR", "Hindex", "TotalDocs3years", "CitesDoc2years", "RefDoc")), 
               list(mean = ~ mean(., na.rm = T),
                    sd = ~ sd(., na.rm = T), 
                    n = ~ sum(!is.na(.)), 
                    se = ~ sd(.,na.rm=TRUE)/sqrt(sum(!is.na(.))),
                    lwr = ~ mean(., na.rm = T) - 1.96*sd(.,na.rm=TRUE)/sqrt(sum(!is.na(.))),
                    upr = ~ mean(., na.rm = T) + 1.96*sd(.,na.rm=TRUE)/sqrt(sum(!is.na(.)))
  )) %>%
  mutate(label = paste0('N = ', SJR_n))

# plot H factor per year
ggplot(hTime, aes(x=year, y = Hindex_mean)) + 
  geom_line() +
  geom_ribbon(aes(ymin=Hindex_lwr,ymax=Hindex_upr),alpha=0.3) +
  geom_point(size = .9) +
  #geom_text(aes(label=label),hjust=0.5, vjust=0) +
  geom_label_repel(data = hTime %>% filter(SJR_n <3),
                  aes(label = label),
                  box.padding   = .35, 
                  point.padding = .5,
                  force         = 10,
                  nudge_y       = -100,
                  segment.color = 'grey50') +
  #geom_histogram(bins = length(unique(dt.Empirical.included$year)), fill = "grey14")+
  ylab("Average H Index")+
  ylim(0, max(hTime$Hindex_upr[hTime$SJR_n>2], na.rm = T)+20) +
  xlab("Year") +
  ggtitle("Average H Index by publication year")+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Line graph of average journal H Index per year.

Figure 26: Line graph of average journal H Index per year. 

# plot average journal citation per year
ggplot(hTime, aes(x=year, y = CitesDoc2years_mean)) + 
  geom_line() +
  geom_ribbon(aes(ymin=CitesDoc2years_lwr,ymax=CitesDoc2years_upr),alpha=0.3) +
  geom_point(size = .9) +
  #geom_text(aes(label=label),hjust=0.5, vjust=0) +
  # geom_label_repel(data = hTime %>% filter(SJR_n <3),
  #                 aes(label = label),
  #                 box.padding   = .35, 
  #                 point.padding = .5,
  #                 force         = 10,
  #                 nudge_y       = -100,
  #                 segment.color = 'grey50') +
  #geom_histogram(bins = length(unique(dt.Empirical.included$year)), fill = "grey14")+
  ylab("Average citations/article (2 years)")+
  ylim(0,10) +
  xlab("Year") +
  ggtitle("Average journal citations/article over time")+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Line graph of the journals' average citation numbers by publication year.

Figure 27: Line graph of the journals’ average citation numbers by publication year.

Journal Keywords

In the Scimago database each journal is assigned a set of keywords capturing their publication topics. Assessing these might offer a better understanding of the types of Journal and readership that are targeted by the authors. We assess the frequency of keyword lists (i.e., combinations of keywords, see Table 44) as well as the frequency of all available individual keywords (see Table 45).

# table of unprocessed journal keywords
data.frame(table(gsub(" \\(Q[0-9]\\)", "", psychDataJournal$Categories))) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Frequency of Raw Journal Keywords",
      format = "html",
      col.names = c("Keyword",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
  scroll_box(height = "500px")
Table 44: Frequency of Raw Journal Keywords
Keyword Frequency
Public Health, Environmental and Occupational Health; Epidemiology 53
Business and International Management; Sociology and Political Science; Social Psychology 43
Social Psychology 23
Psychology (miscellaneous) 17
Anthropology; Cultural Studies; Social Psychology 14
Social Psychology; Sociology and Political Science 12
Arts and Humanities (miscellaneous); Medicine (miscellaneous); Psychology (miscellaneous) 10
Medicine (miscellaneous); Psychiatry and Mental Health 9
Clinical Psychology; Psychiatry and Mental Health 8
Nursing (miscellaneous) 8
Psychiatry and Mental Health 8
Public Health, Environmental and Occupational Health 8
Arts and Humanities (miscellaneous); Psychology (miscellaneous) 6
Psychiatric Mental Health 6
Social Psychology; Social Work 6
Health (social science); Public Health, Environmental and Occupational Health 5
Arts and Humanities (miscellaneous); Psychology (miscellaneous); Developmental and Educational Psychology; Psychiatry and Mental Health 4
Education; Health (social science); Social Work 4
Education; Sociology and Political Science 4
Health (social science); History and Philosophy of Science; Medicine (miscellaneous) 4
Health (social science); Psychiatry and Mental Health 4
Health (social science); Social Work; Applied Psychology; Public Health, Environmental and Occupational Health 4
Multidisciplinary 4
Sociology and Political Science 4
Anthropology; Social Sciences (miscellaneous) 3
Arts and Humanities (miscellaneous); Cultural Studies; Public Health, Environmental and Occupational Health 3
Arts and Humanities (miscellaneous); Social Sciences (miscellaneous); Sociology and Political Science; Developmental and Educational Psychology 3
Developmental and Educational Psychology; Education 3
Developmental and Educational Psychology; Education; Life-span and Life-course Studies; Social Psychology; Social Sciences (miscellaneous); Developmental Neuroscience 3
Developmental and Educational Psychology; Education; Pediatrics, Perinatology and Child Health; Social Work 3
Epidemiology; Public Health, Environmental and Occupational Health 3
Gerontology; Psychiatric Mental Health; Psychiatry and Mental Health; Geriatrics and Gerontology 3
Health (social science); Geriatrics and Gerontology 3
Public Health, Environmental and Occupational Health; Social Psychology 3
Sociology and Political Science; Social Psychology 3
Aging; Developmental and Educational Psychology; Geriatrics and Gerontology 2
Anthropology; Arts and Humanities (miscellaneous); Psychology (miscellaneous) 2
Anthropology; Cultural Studies; Linguistics and Language; Social Psychology 2
Anthropology; Cultural Studies; Sociology and Political Science 2
Applied Psychology 2
Applied Psychology; Social Psychology 2
Business, Management and Accounting (miscellaneous); Education; Psychology (miscellaneous) 2
Clinical Psychology; Applied Psychology 2
Communication; Strategy and Management 2
Community and Home Care; Gerontology; Health (social science); Life-span and Life-course Studies; Sociology and Political Science; Geriatrics and Gerontology 2
Demography; Developmental and Educational Psychology; Life-span and Life-course Studies 2
Developmental and Educational Psychology; Education; Social Psychology; Social Sciences (miscellaneous) 2
Developmental and Educational Psychology; Life-span and Life-course Studies 2
Developmental and Educational Psychology; Pediatrics, Perinatology and Child Health; Psychiatry and Mental Health; Social Psychology 2
Epidemiology; Health (social science); Psychiatry and Mental Health; Social Psychology 2
Health (social science); Medicine (miscellaneous); Psychiatry and Mental Health; Public Health, Environmental and Occupational Health 2
Health Policy; Health (social science); Public Health, Environmental and Occupational Health 2
Health Professions (miscellaneous) 2
Infectious Diseases; Public Health, Environmental and Occupational Health; Social Psychology 2
Life-span and Life-course Studies 2
Life-span and Life-course Studies; Social Sciences (miscellaneous); Sociology and Political Science 2
Medicine (miscellaneous) 2
Social Sciences (miscellaneous) 2
Social Sciences (miscellaneous); Social Psychology 2
Social Sciences (miscellaneous); Social Work 2
Social Sciences (miscellaneous); Sociology and Political Science 2
Social Sciences (miscellaneous); Sociology and Political Science; Social Work 2
Anthropology; Arts and Humanities (miscellaneous); Health (social science); Medicine (miscellaneous); Psychiatry and Mental Health 1
Anthropology; Arts and Humanities (miscellaneous); Social Sciences (miscellaneous) 1
Anthropology; Arts and Humanities (miscellaneous); Social Sciences (miscellaneous); Social Work 1
Anthropology; Social Psychology; Sociology and Political Science 1
Anthropology; Sociology and Political Science 1
Applied Psychology; Clinical Psychology 1
Applied Psychology; Communication; Computer Science Applications; Human-Computer Interaction; Medicine (miscellaneous); Social Psychology 1
Applied Psychology; Cultural Studies 1
Applied Psychology; Education; Life-span and Life-course Studies; Organizational Behavior and Human Resource Management 1
Applied Psychology; Management Science and Operations Research; Organizational Behavior and Human Resource Management; Social Psychology 1
Applied Psychology; Psychology (miscellaneous); Social Psychology 1
Arts and Humanities (miscellaneous); Clinical Psychology; Health, Toxicology and Mutagenesis; Psychiatry and Mental Health 1
Arts and Humanities (miscellaneous); Developmental and Educational Psychology; Medicine (miscellaneous); Psychology (miscellaneous) 1
Arts and Humanities (miscellaneous); Psychology (miscellaneous); Social Psychology; Sociology and Political Science 1
Biological Psychiatry; Psychiatry and Mental Health 1
Business and International Management; Communication; Marketing 1
Business and International Management; Cultural Studies; Sociology and Political Science; Strategy and Management; Organizational Behavior and Human Resource Management 1
Business and International Management; Marketing 1
Business and International Management; Organizational Behavior and Human Resource Management 1
Business, Management and Accounting (miscellaneous); Political Science and International Relations; Sociology and Political Science 1
Clinical Psychology; Experimental and Cognitive Psychology 1
Clinical Psychology; Geriatrics and Gerontology; Gerontology; Health (social science); Life-span and Life-course Studies; Medicine (miscellaneous); Social Psychology; Sociology and Political Science 1
Clinical Psychology; Gerontology; Health (social science); Social Psychology; Geriatrics and Gerontology 1
Clinical Psychology; Medicine (miscellaneous); Psychiatry and Mental Health; Social Psychology 1
Communication 1
Communication; Language and Linguistics 1
Communication; Social Psychology; Sociology and Political Science; Developmental and Educational Psychology 1
Community and Home Care; Gerontology; Health (social science); Geriatrics and Gerontology 1
Cultural Studies; Anthropology; Sociology and Political Science 1
Cultural Studies; Education; Social Sciences (miscellaneous); Sociology and Political Science; Social Psychology 1
Developmental and Educational Psychology 1
Developmental and Educational Psychology; Life-span and Life-course Studies; Social Sciences (miscellaneous); Sociology and Political Science 1
Developmental and Educational Psychology; Psychiatry and Mental Health 1
Education; Applied Psychology; Developmental and Educational Psychology; Psychology (miscellaneous) 1
Education; E-learning 1
Education; Medicine (miscellaneous); Psychiatry and Mental Health 1
Epidemiology; Psychiatry and Mental Health 1
Experimental and Cognitive Psychology; Sensory Systems 1
Gender Studies; Law; Sociology and Political Science 1
Health (social science); Maternity and Midwifery; Obstetrics and Gynecology; Public Health, Environmental and Occupational Health 1
Health Policy; Nutrition and Dietetics; Pediatrics, Perinatology and Child Health; Public Health, Environmental and Occupational Health 1
History; Literature and Literary Theory; Social Sciences (miscellaneous); Education 1
Issues, Ethics and Legal Aspects; Health Policy; Medicine (miscellaneous) 1
Language and Linguistics; Linguistics and Language; Psychology (miscellaneous) 1
Law; Health Policy; Health (social science); Public Health, Environmental and Occupational Health 1
Law; Social Sciences (miscellaneous); Sociology and Political Science; Clinical Psychology 1
Life-span and Life-course Studies; Applied Psychology; Developmental and Educational Psychology 1
Marketing 1
Medicine (miscellaneous); Nursing (miscellaneous) 1
Medicine (miscellaneous); Nutrition and Dietetics 1
Medicine (miscellaneous); Psychiatry and Mental Health; Toxicology 1
Medicine (miscellaneous); Public Health, Environmental and Occupational Health 1
Nursing (miscellaneous); Medicine (miscellaneous) 1
Nursing (miscellaneous); Social Sciences (miscellaneous); Social Work 1
Nutrition and Dietetics; Psychology (miscellaneous) 1
Obstetrics and Gynecology; Pediatrics, Perinatology and Child Health; Public Health, Environmental and Occupational Health; Epidemiology 1
Organizational Behavior and Human Resource Management; Social Sciences (miscellaneous) 1
Orthopedics and Sports Medicine 1
Pharmacology; Pharmacology (medical); Psychiatry and Mental Health; Toxicology 1
Physical Therapy, Sports Therapy and Rehabilitation; Sports Science 1
Psychiatry and Mental Health; Biological Psychiatry 1
Psychiatry and Mental Health; Psychology (miscellaneous) 1
Public Health, Environmental and Occupational Health; Applied Psychology 1
Public Health, Environmental and Occupational Health; Oncology 1
Religious Studies 1
Religious Studies; Clinical Psychology; Health (social science); Psychiatry and Mental Health 1
Safety, Risk, Reliability and Quality; Physiology (medical) 1
Social Sciences (miscellaneous); Sociology and Political Science; Sports Science 1
Sociology and Political Science; Environmental Science (miscellaneous); Tourism, Leisure and Hospitality Management 1
Strategy and Management; Tourism, Leisure and Hospitality Management 1 
# table of individual journal keywords
data.frame(table(unlist(str_split(gsub(" \\(Q[0-9]\\)", "", psychDataJournal$Categories), "; ")))) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Frequency of Individual Journal Keywords",
      format = "html",
      col.names = c("Keyword",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
  add_footnote("_Note_ that journals can have multiple associated keywords.", notation = "none") %>%
  scroll_box(height = "500px")
Table 45: Frequency of Individual Journal Keywords
Keyword Frequency
Social Psychology 131
Sociology and Political Science 93
Public Health, Environmental and Occupational Health 92
Epidemiology 60
Psychiatry and Mental Health 54
Psychology (miscellaneous) 48
Business and International Management 47
Health (social science) 39
Medicine (miscellaneous) 39
Arts and Humanities (miscellaneous) 34
Developmental and Educational Psychology 33
Social Sciences (miscellaneous) 32
Anthropology 29
Education 27
Cultural Studies 25
Social Work 23
Applied Psychology 19
Clinical Psychology 18
Life-span and Life-course Studies 17
Geriatrics and Gerontology 13
Nursing (miscellaneous) 11
Psychiatric Mental Health 9
Gerontology 8
Communication 7
Pediatrics, Perinatology and Child Health 7
Health Policy 5
Organizational Behavior and Human Resource Management 5
History and Philosophy of Science 4
Multidisciplinary 4
Strategy and Management 4
Business, Management and Accounting (miscellaneous) 3
Community and Home Care 3
Developmental Neuroscience 3
Law 3
Linguistics and Language 3
Marketing 3
Nutrition and Dietetics 3
Aging 2
Biological Psychiatry 2
Demography 2
Experimental and Cognitive Psychology 2
Health Professions (miscellaneous) 2
Infectious Diseases 2
Language and Linguistics 2
Obstetrics and Gynecology 2
Religious Studies 2
Sports Science 2
Tourism, Leisure and Hospitality Management 2
Toxicology 2
Computer Science Applications 1
E-learning 1
Environmental Science (miscellaneous) 1
Gender Studies 1
Health, Toxicology and Mutagenesis 1
History 1
Human-Computer Interaction 1
Issues, Ethics and Legal Aspects 1
Literature and Literary Theory 1
Management Science and Operations Research 1
Maternity and Midwifery 1
Oncology 1
Orthopedics and Sports Medicine 1
Pharmacology 1
Pharmacology (medical) 1
Physical Therapy, Sports Therapy and Rehabilitation 1
Physiology (medical) 1
Political Science and International Relations 1
Safety, Risk, Reliability and Quality 1
Sensory Systems 1
Note that journals can have multiple associated keywords.

Coding

Beyond the keywords, the Scimago database classifies each journal according to the field(s) that the journal aims to address. These field codes aim to capture a higher level of academic classification (than the keywords). Importantly, (1) each journal can be be classified to address multiple fields and (2) the field include codes of fields (e.g., ‘Social Sciences’) as well as sub-fields (e.g., ‘Social Psychology’). This leads to the case that there can be quite a lot of overlap between fields and journals cannot easily or readily be assessed in mutually exclusive subgroups. Yet, one of the aims of this review is to assess differences between fields and disciplines. We aim to address this issue in the following section. However, we first need to gain a better understanding of the fields that are addressed by the journals. We list the frequencies of all unique combinations (see Table 46, as well as the overall frequencies of all available field codes (see Table 47).

# table of unprocessed journal field codes
data.frame(table(psychDataJournal$fields)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Frequency of Raw Journal Fields",
      format = "html",
      col.names = c("Field Code Combination",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 46: Frequency of Raw Journal Fields
Field Code Combination Frequency
Medicine 88
Psychology; Social Sciences 56
Psychology 50
Business, Management and Accounting; Psychology; Social Sciences 46
Social Sciences 34
Medicine; Psychology 19
Medicine; Social Sciences 18
Arts and Humanities; Medicine; Psychology 15
Nursing 14
Medicine; Psychology; Social Sciences 9
Arts and Humanities; Medicine; Social Sciences 8
Medicine; Nursing 8
Arts and Humanities; Psychology; Social Sciences 7
Arts and Humanities; Psychology 6
Business, Management and Accounting; Social Sciences 6
Arts and Humanities; Social Sciences 4
Business, Management and Accounting 4
Medicine; Nursing; Social Sciences 4
Multidisciplinary 4
Health Professions 3
Neuroscience; Psychology; Social Sciences 3
Biochemistry, Genetics and Molecular Biology; Medicine; Psychology 2
Medicine; Neuroscience 2
Medicine; Nursing; Psychology; Social Sciences 2
Medicine; Pharmacology, Toxicology and Pharmaceutics 2
Arts and Humanities 1
Arts and Humanities; Environmental Science; Medicine; Psychology 1
Arts and Humanities; Medicine; Psychology; Social Sciences 1
Business, Management and Accounting; Decision Sciences; Psychology 1
Business, Management and Accounting; Environmental Science; Social Sciences 1
Computer Science; Medicine; Psychology; Social Sciences 1
Engineering; Medicine 1
Health Professions; Social Sciences 1
Neuroscience; Psychology 1
Nursing; Psychology 1
Nursing; Social Sciences 1 
# table of individual journal fields
data.frame(table(unlist(str_split(psychDataJournal$fields, "; ")))) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Frequency of Individual Journal Fields",
      format = "html",
      col.names = c("Field Code",
                    "Frequency")) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
  add_footnote("_Note_ that journals can target multitple fields.", notation = "none")
Table 47: Frequency of Individual Journal Fields
Field Code Frequency
Psychology 221
Social Sciences 202
Medicine 181
Business, Management and Accounting 58
Arts and Humanities 43
Nursing 30
Neuroscience 6
Health Professions 4
Multidisciplinary 4
Biochemistry, Genetics and Molecular Biology 2
Environmental Science 2
Pharmacology, Toxicology and Pharmaceutics 2
Computer Science 1
Decision Sciences 1
Engineering 1
Note that journals can target multitple fields.

To summarize the articles further we then classified the field combinations into superordinate discipline codes. These discipline codes are based in part on U.S. Department of Education’s National Center for Education Statistics Classification of Instructional Programs (CIP), the U.K. Higher Education Statistics Agency Joint Academic Coding System (JACS 3.0), the Australian Bureau of Statistics’ Australian and New Zealand Standard Research Classification (ANZSRC 2020), as well as the Fields of Knowledge Map from the ‘Things Made Thinkable’ initiative.

In a first step we used the highest categories available (Social Sciences, Arts and Humanities, Professions and Applied Sciences, Natural Sciences, Formal Sciences, and Multidisciplinary) to summarize the fields. The Scimago database included some superordinate discipline codes (i.e., Social Sciences, Arts and Humanities) so we used these if a single discipline code was chosen by a journal. We then re-coded the fields that did not have a super-ordinate discipline code or had multiple (see Table 48, for the disciplines associated to each field combination).

# Create re-code categories
# Fields that fall within the Social Sciences 
socialSciences <- c("Business, Management and Accounting; Environmental Science; Social Sciences",
                    "Business, Management and Accounting; Psychology; Social Sciences", 
                    "Business, Management and Accounting; Social Sciences",
                    "Health Professions; Social Sciences",
                    "Medicine; Nursing; Psychology; Social Sciences",
                    "Medicine; Nursing; Social Sciences",
                    "Medicine; Psychology; Social Sciences",
                    "Medicine; Social Sciences",
                    "Neuroscience; Psychology; Social Sciences",
                    "Nursing; Social Sciences",
                    "Psychology",
                    "Psychology; Social Sciences",
                    "Social Sciences")

# Fields that fall within the Arts and Humanities
artsHumanities <- c("Arts and Humanities; Medicine; Psychology",
                    "Arts and Humanities; Psychology")

# Fields that fall within the Natural Sciences
naturalSciences <- c()

# Fields that fall within the Formal Sciences
formalSciences <- c()

# Fields that fall within the Professional and Applied Sciences
professionsAppliedSciences <- c("Business, Management and Accounting",
                                "Medicine", 
                                "Medicine; Neuroscience",
                                "Medicine; Nursing",
                                "Nursing")

# Field combinations that fall within multiple broader categories
crossDisciplinary <- c("Arts and Humanities; Medicine; Social Sciences",
                       "Arts and Humanities; Psychology; Social Sciences",
                       "Arts and Humanities; Social Sciences",
                       "Biochemistry, Genetics and Molecular Biology; Medicine; Psychology", 
                       "Business, Management and Accounting; Decision Sciences; Psychology",
                       "Medicine; Psychology",
                       "Multidisciplinary")

# other
other <- c()

# create database with discipline and field codes
disciplines01 <- data.frame(fields = c(socialSciences, 
                                       artsHumanities, 
                                       naturalSciences, 
                                       formalSciences, 
                                       professionsAppliedSciences, 
                                       crossDisciplinary, 
                                      other),
                         discipline01 = c(rep("Social Sciences", length(socialSciences)),
                                          rep("Arts and Humanities", length(artsHumanities)),
                                          rep("Natural Sciences", length(naturalSciences)),
                                          rep("Formal Sciences", length(formalSciences)),
                                          rep("Professions and Applied Sciences", length(professionsAppliedSciences)),
                                          rep("Multidisciplinary / Crossdisciplinary", length(crossDisciplinary)),
                                          rep("Other", length(other)))
                         )

# add discipline codes to empirical studies
empiricalDataDisciplines <- right_join(psychDataJournal, disciplines01)

# make table of Field to Discipline relations
disciplines01 %>%
  kbl(., caption = "Coding Schema for Disciplines (Version #1)",
      format = "html",
      col.names = c("Fields",
                    "Discipline #1")) %>%
  kable_classic(full_width = F,
                lightable_options = "hover",
                html_font = "Cambria")
Table 48: Coding Schema for Disciplines (Version #1)
Fields Discipline #1
Business, Management and Accounting; Environmental Science; Social Sciences Social Sciences
Business, Management and Accounting; Psychology; Social Sciences Social Sciences
Business, Management and Accounting; Social Sciences Social Sciences
Health Professions; Social Sciences Social Sciences
Medicine; Nursing; Psychology; Social Sciences Social Sciences
Medicine; Nursing; Social Sciences Social Sciences
Medicine; Psychology; Social Sciences Social Sciences
Medicine; Social Sciences Social Sciences
Neuroscience; Psychology; Social Sciences Social Sciences
Nursing; Social Sciences Social Sciences
Psychology Social Sciences
Psychology; Social Sciences Social Sciences
Social Sciences Social Sciences
Arts and Humanities; Medicine; Psychology Arts and Humanities
Arts and Humanities; Psychology Arts and Humanities
Business, Management and Accounting Professions and Applied Sciences
Medicine Professions and Applied Sciences
Medicine; Neuroscience Professions and Applied Sciences
Medicine; Nursing Professions and Applied Sciences
Nursing Professions and Applied Sciences
Arts and Humanities; Medicine; Social Sciences Multidisciplinary / Crossdisciplinary
Arts and Humanities; Psychology; Social Sciences Multidisciplinary / Crossdisciplinary
Arts and Humanities; Social Sciences Multidisciplinary / Crossdisciplinary
Biochemistry, Genetics and Molecular Biology; Medicine; Psychology Multidisciplinary / Crossdisciplinary
Business, Management and Accounting; Decision Sciences; Psychology Multidisciplinary / Crossdisciplinary
Medicine; Psychology Multidisciplinary / Crossdisciplinary
Multidisciplinary Multidisciplinary / Crossdisciplinary

Categorization in this – highest order – manner led to the following subgroup frequencies (see Table 49):

# Frequencies of discipline codes
data.frame(table(empiricalDataDisciplines$discipline01)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Discipline Frequency (Version #1)",
      format = "html",
      col.names = c("Discipline #1",
                    "Frequency")) %>%
  kable_classic(full_width = F,
                lightable_options = "hover",
                html_font = "Cambria")
Table 49: Discipline Frequency (Version #1)
Discipline #1 Frequency
Social Sciences 231
Professions and Applied Sciences 116
Multidisciplinary / Crossdisciplinary 45
Arts and Humanities 21

This higher-order discipline-coding had two major concerns. Firstly, the category of ‘Social Sciences’ was too large and heterogeneous and secondly, the medical and health related fields were not sufficiently distinguished from the other categories (i.e., either as part of ‘Social Sciences’ or ‘Applied Sciences’). We, therefore, created a second discipline coding breaking the ‘Social Sciences’ and ‘Applied Sciences’ categories into major fields (psychology; business; medicine, nursing, & health). We retained the possibility of miscellaneous social sciences, arts and humanities, as well as multidisciplinary field codes (see Table 50, for the discipline codes associated with each fields code).

# Create re-code categories
# Fields that fall within the Psychological Sciences 
psychology <- c("Arts and Humanities; Psychology",
                "Arts and Humanities; Psychology; Social Sciences",
                "Psychology",
                "Psychology; Social Sciences",
                "Neuroscience; Psychology; Social Sciences")

# Fields that fall within the Business discipline 
business <- c("Business, Management and Accounting",
              "Business, Management and Accounting; Environmental Science; Social Sciences",
              "Business, Management and Accounting; Social Sciences")

# Fields that fall within the Medical Sciences 
medical <- c("Arts and Humanities; Medicine; Social Sciences",
             "Health Professions; Social Sciences",
             "Medicine", 
             "Medicine; Neuroscience",
             "Medicine; Nursing",
             "Medicine; Nursing; Social Sciences",
             "Medicine; Social Sciences",
             "Nursing",
             "Nursing; Social Sciences")

# Fields that fall within the Social Sciences 
social <- c("Social Sciences")

# Fields that fall within the Arts 
arts <- c()

# studies that fall within multiple disciplines
multi <- c("Arts and Humanities; Medicine; Psychology",
           "Arts and Humanities; Social Sciences",
           "Biochemistry, Genetics and Molecular Biology; Medicine; Psychology", 
           "Business, Management and Accounting; Decision Sciences; Psychology",
           "Business, Management and Accounting; Psychology; Social Sciences",
           "Medicine; Psychology",
           "Medicine; Psychology; Social Sciences",
           "Medicine; Nursing; Psychology; Social Sciences",
           "Multidisciplinary")

# create database with discipline and field codes
disciplines02 <- data.frame(fields = c(psychology, 
                                       medical, 
                                       business, 
                                       social, 
                                       arts, 
                                       multi),
                            discipline02 = c(rep("Psychology", length(psychology)),
                                             rep("Medicine, Nursing, & Health", length(medical)),
                                             rep("Business", length(business)),
                                             rep("Social Sciences (miscellaneous)", length(social)),
                                             rep("Arts and Humanities", length(arts)),
                                             rep("Multidisciplinary / Crossdisciplinary", length(multi)))
                         )


# add new discipline codes to empirical studies
empiricalDataDisciplines <- right_join(empiricalDataDisciplines, disciplines02)

# make table of new Field to Discipline relations
disciplines02 %>%
  kbl(., caption = "Coding Schema for Disciplines (Version #2)",
      format = "html",
      col.names = c("Fields",
                    "Discipline #2")) %>%
  kable_classic(full_width = F,
                lightable_options = "hover",
                html_font = "Cambria")
Table 50: Coding Schema for Disciplines (Version #2)
Fields Discipline #2
Arts and Humanities; Psychology Psychology
Arts and Humanities; Psychology; Social Sciences Psychology
Psychology Psychology
Psychology; Social Sciences Psychology
Neuroscience; Psychology; Social Sciences Psychology
Arts and Humanities; Medicine; Social Sciences Medicine, Nursing, & Health
Health Professions; Social Sciences Medicine, Nursing, & Health
Medicine Medicine, Nursing, & Health
Medicine; Neuroscience Medicine, Nursing, & Health
Medicine; Nursing Medicine, Nursing, & Health
Medicine; Nursing; Social Sciences Medicine, Nursing, & Health
Medicine; Social Sciences Medicine, Nursing, & Health
Nursing Medicine, Nursing, & Health
Nursing; Social Sciences Medicine, Nursing, & Health
Business, Management and Accounting Business
Business, Management and Accounting; Environmental Science; Social Sciences Business
Business, Management and Accounting; Social Sciences Business
Social Sciences Social Sciences (miscellaneous)
Arts and Humanities; Medicine; Psychology Multidisciplinary / Crossdisciplinary
Arts and Humanities; Social Sciences Multidisciplinary / Crossdisciplinary
Biochemistry, Genetics and Molecular Biology; Medicine; Psychology Multidisciplinary / Crossdisciplinary
Business, Management and Accounting; Decision Sciences; Psychology Multidisciplinary / Crossdisciplinary
Business, Management and Accounting; Psychology; Social Sciences Multidisciplinary / Crossdisciplinary
Medicine; Psychology Multidisciplinary / Crossdisciplinary
Medicine; Psychology; Social Sciences Multidisciplinary / Crossdisciplinary
Medicine; Nursing; Psychology; Social Sciences Multidisciplinary / Crossdisciplinary
Multidisciplinary Multidisciplinary / Crossdisciplinary

Re-categorization led to the following subgroup frequencies (see Table 51):

# Frequencies of new discipline codes
data.frame(table(empiricalDataDisciplines$discipline02)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Discipline Frequency (Version #2)",
      format = "html",
      col.names = c("Discipline #2",
                    "Frequency")) %>%
  kable_classic(full_width = F,
                lightable_options = "hover",
                html_font = "Cambria")
Table 51: Discipline Frequency (Version #2)
Discipline #2 Frequency
Medicine, Nursing, & Health 144
Psychology 122
Multidisciplinary / Crossdisciplinary 102
Social Sciences (miscellaneous) 34
Business 11

Given the small number of ‘Business’ journals we reclassified these into the ‘Social Sciences (miscellaneous)’ category (see Table 52 and Figure 28).

empiricalDataDisciplines$discipline02[empiricalDataDisciplines$discipline02 == "Business"] <- "Social Sciences (miscellaneous)"

data.frame(table(empiricalDataDisciplines$discipline02)) %>%
  arrange(desc(Freq)) %>%
  kbl(., caption = "Discipline Frequency Ajusted",
      format = "html",
      col.names = c("Discipline #2 (adjusted)",
                    "Frequency")) %>%
  kable_classic(full_width = F,
                lightable_options = "hover",
                html_font = "Cambria")
Table 52: Discipline Frequency Ajusted
Discipline #2 (adjusted) Frequency
Medicine, Nursing, & Health 144
Psychology 122
Multidisciplinary / Crossdisciplinary 102
Social Sciences (miscellaneous) 45 
# tally links from number of combinations
links <- empiricalDataDisciplines %>%
  dplyr::select(source = fields, target = discipline02) %>%
  mutate(target = str_replace(target, "Psychology", "Psychology ")) %>%
  group_by(source, target) %>%
  summarise(value = n()) %>%
  arrange(desc(value))

# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodes <- data.frame(
  name=c(as.character(links$source),
  as.character(links$target)) %>% unique()
)

# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
links$IDsource <- match(links$source, nodes$name)-1
links$IDtarget <- match(links$target, nodes$name)-1

# Make the Network
sankeyNetwork(Links = links, Nodes = nodes,
              Source = "IDsource", Target = "IDtarget",
              Value = "value", NodeID = "name",
              sinksRight=F, height= 800)

Figure 28: Sankey Graph fields to discipline re-coding. 

# export cleaned datasets for methods and results section
save(dt.Input, 
     dt.Scales, dt.Scales.Included, 
     dt.Empirical, dt.Empirical.included, psychData, 
     dt.Theories, dt.Theories.Included, dt.TheorySearch, 
     dt.Domains, empiricalDataDisciplines, file = "data/wrangled.RData")

save(dt.Scales, dt.Scales.Included, dt.Theories, dt.Theories.Included, file = "data/AcculturationScales.RData")

# export reduced dataset for OSF repository
theorySearchCleand <- dt.TheorySearch %>%
  dplyr::select(-c(AuthorFull,TitleAlphaNum, 
                   TitleScreening, TitleNote, AbstractScreening, AbstractNote, 
                   SearchDuplicateDOI, SearchDuplicateTitle, DuplicateAny, 
                   ID, IDDuplicate,
                   Downloaded, DownloadReason, Extracted, ExtractedReason, TheoryName, Comment)) 
write.csv(theorySearchCleand,'data/OSF/theoretical01_SearchCleaned.csv')

theorySearchScreened <- dt.TheorySearch %>%
  dplyr::select(-c(AuthorFull,TitleAlphaNum))
write.csv(theorySearchScreened,'data/OSF/theoretical02_SearchScreened.csv')

write.csv(dt.Theories.Included, 'data/OSF/theoretical03_DatabaseCoded.csv')

write.csv(dt.Scales, 'data/OSF/methodological01_SearchCleaned.csv')
write.csv(dt.Scales.Included, 'data/OSF/methodological03_DatabaseCoded.csv')


empiricalSearchCleand <- dt.Empirical %>%
  dplyr::select(-c(TitleScreening,  TitleNote, AbstractScreening, AbstractNote, SearchDuplicate,
                   ID,  IDDuplicate,    CitationKey,    Downloaded, empirical,  Method, term,   MeasureDefinition,
                   Affect,  Behavior,   Cognition,  Desire, Affect2,    Behavior2,  Cognition2, Desire2, 
                   AffectFinal, BehaviorFinal, CognitionFinal, DesireFinal,
                   MissingABCD, NoteMissing,    TypeComplexity, MeasurementLevels,  domainScale,    domainPaper,
                   MainAnalysis,    VariableType,   Sample, MigrationTime,  IncludesMajority,
                   `Host Country 1`,    `Host Country 2`,   `Host Country 3`,   `Host Country 4`,   HostCountry,
                   `Origin Country 1`,  `Origin Country 2`, `Origin Country 3`, `Origin Country 4`, `Origin Country 5`, `Origin Country 6`, OriginCountry,  Comment))
write.csv(empiricalSearchCleand,'data/OSF/empirical01_SearchCleaned.csv')

empiricalSearchScreened <- dt.Empirical %>%
  dplyr::select(-c(empirical,   Method, term,   MeasureDefinition,
                   Affect,  Behavior,   Cognition,  Desire, Affect2,    Behavior2,  Cognition2, Desire2,
                   AffectFinal, BehaviorFinal, CognitionFinal, DesireFinal,
                   TypeComplexity,  MeasurementLevels,  domainScale,    domainPaper,
                   MainAnalysis,    VariableType,   Sample, MigrationTime,  IncludesMajority,
                   `Host Country 1`,    `Host Country 2`,   `Host Country 3`,   `Host Country 4`,   HostCountry,
                   `Origin Country 1`,  `Origin Country 2`, `Origin Country 3`, `Origin Country 4`, `Origin Country 5`, `Origin Country 6`, OriginCountry))
write.csv(empiricalSearchScreened,'data/OSF/empirical02_SearchScreened.csv')

write.csv(dt.Empirical.included, 'data/OSF/empirical03_DatabaseCoded.csv')

# export numbers for Coding Protocol Fact Sheets
## Theoretical:
nTheo <- nrow(dt.Theories.Included)
percTheoMissing <- ((nrow(dt.Theories) - nrow(dt.Theories.Included)) / nrow(dt.Theories) * 100) %>%
  round(.,2)
nTheories <- dt.Theories.Included %>%
  filter(FrameworkTheoryModel == "Theory") %>%
  nrow
percTheories <- ((nTheories/nTheo) * 100) %>%
  round(.,1)
nFrameworks <- dt.Theories.Included %>%
  filter(FrameworkTheoryModel == "Framework") %>%
  nrow
percFrameworks <- ((nFrameworks/nTheo) * 100) %>%
  round(.,1)
nModels <- dt.Theories.Included %>%
  filter(FrameworkTheoryModel == "Model") %>%
  nrow
percModels <- ((nModels/nTheo) * 100) %>%
  round(.,1)
nConceptualizations <- dt.Theories.Included %>%
  filter(FrameworkTheoryModel == "Conceptualization") %>%
  nrow
percConceptualizations <- ((nConceptualizations/nTheo) * 100) %>%
  round(.,1)

cat("\\def \\nTheo {",nTheo,"}
\\def \\percTheoMissing {", percTheoMissing, "}
\\def \\nTheories {", nTheories, "}
\\def \\percTheories {", percTheories, "}
\\def \\nFrameworks {", nFrameworks, "}
\\def \\percFrameworks {", percFrameworks, "}
\\def \\nModels {", nModels, "}
\\def \\percModels {", percModels, "}
\\def \\nConceptualizations {", percTheoMissing, "}
\\def \\percTheoMissing {", percConceptualizations, "}",
    sep = "", 
    file = "Supplemental Material A - Coding Protocol/margins/theoNums.tex")
    
## Methodological:
nScales <- nrow(dt.Scales.Included)
nAllScales <- nrow(dt.Scales)
nMethInaccess <- scalesExcl %>%
  filter(grepl("accessible", `Exclusion Reason`)) %>%
  select(`Full Text`) %>%
  sum
percMethInaccess <- (nMethInaccess / (nMethInaccess + nScales) * 100) %>%
  round(.,2)

cat("\\def \\nScales {",nScales,"}
\\def \\nAllScales {", nAllScales, "}
\\def \\nMethInaccess {", nMethInaccess, "}
\\def \\percMethInaccess {", percMethInaccess, "}",
    sep = "", 
    file = "Supplemental Material A - Coding Protocol/margins/methNums.tex")

## Empirical:
nEmp <- nrow(dt.Empirical.included)
nAllEmp <- nrow(dt.Empirical.unique)
nEmpInaccess <- empiricalExclFull %>% 
  filter(grepl("accessible", Exclusion)) %>%
  select(Freq) %>%
  sum
percEmpInaccess <- (nEmpInaccess / (nEmpInaccess + nEmp) * 100) %>%
  round(., 0)
nEmpItemInaccess <- empiricalExclFull %>% 
  filter(grepl("items not accessible", Exclusion)) %>%
  select(Freq) %>%
  as.numeric
percEmpItemInaccess <- (nEmpItemInaccess / (nEmpInaccess + nEmp) * 100) %>%
  round(., 0)
nEmpThesisInaccess <- empiricalExclFull %>% 
  filter(grepl("thesis not accessible", Exclusion)) %>%
  select(Freq) %>%
  as.numeric
percEmpThesisInaccess <- (nEmpThesisInaccess / (nEmpInaccess + nEmp) * 100) %>%
  round(., 0)
nJournal <- dt.Empirical.included %>%
  filter(PublicationType == "journalArticle") %>%
  nrow
percJournal <- ((nJournal/nEmp) * 100) %>%
  round(.,1)
nThesis <- dt.Empirical.included %>%
  filter(PublicationType == "thesis") %>%
  nrow
percThesis <- ((nThesis/nEmp) * 100) %>%
  round(.,1)
nChapter <- dt.Empirical.included %>%
  filter(PublicationType == "bookSection") %>%
  nrow
percChapter <- ((nChapter/nEmp) * 100) %>%
  round(.,1)

cat("\\def \\nEmp {",nEmp,"}
\\def \\nAllEmp {",nAllEmp,"}
\\def \\nEmpInaccess {", nEmpInaccess, "}
\\def \\percEmpInaccess {", percEmpInaccess, "}
\\def \\nEmpItemInaccess {", nEmpItemInaccess, "}
\\def \\percEmpItemInaccess {", percEmpItemInaccess, "}
\\def \\nEmpThesisInaccess {", nEmpThesisInaccess, "}
\\def \\percEmpThesisInaccess {", percEmpThesisInaccess, "}
\\def \\nJournal {", nJournal, "}
\\def \\percJournal {", percJournal, "}
\\def \\nThesis {", nThesis, "}
\\def \\percThesis {", percThesis, "}
\\def \\nChapter {", nChapter, "}
\\def \\percChapter {", percChapter, "}",
    sep = "", 
    file = "Supplemental Material A - Coding Protocol/margins/empNums.tex")

cat("\\def \\nTheo {",nTheo,"}
\\def \\nScales {",nScales,"}
\\def \\nEmp {", nEmp, "}",
    sep = "", 
    file = "Supplemental Material A - Coding Protocol/margins/nNums.tex")

This coding resulted in 4 relatively well-balanced categories with non of the fields having too few observations. In the following sections we use these discipline codes to assess some general differences in assessing and addressing acculturation.

Descriptives

terms, collection methods, measures, year of publication, focus of paper, variable type

Acculturation Terms

We first assess which terms the different disciplines use most frequently to describe the cultural adaptation process. We list the main terms the authors used in their paper to refer to cultural adaptation in Table 53.

# Frequency Table of acculturation terms by field
empiricalDataDisciplines %>%
  dplyr::select(discipline02,term) %>%
  mutate(term = tolower(term)) %>%
  group_by(discipline02, Term = term) %>%
  summarise(Frequency=n()) %>%
  spread(discipline02, Frequency) %>%
  arrange(desc(Psychology)) %>%
  ungroup %>%
  mutate_if(is.numeric, funs(replace_na(as.character(.), ""))) %>%
  dplyr::select(Term, Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  kbl(., caption = "Terms used per discipline",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 53: Terms used per discipline
Term Psychology Medicine, Nursing, & Health Social Sciences (miscellaneous) Multidisciplinary / Crossdisciplinary
acculturation 86 123 24 57
acculturation attitudes 5 4
adjustment 3 2 1
assimilation 3 3 5 6
ethnic identity 3
integration 3 4 7 5
acculturation orientation 2 1
acculturation preferences 2 1
aculturative stress 2 3
emotional acculturation 2
psychological adjustment 2 1 1 1
social integration 2 3 2 4
acculturation patterns 1 1
acculturative orientation 1
adaptation 1 1 1 3
cultural identity change 1
migration 1
sociocultural adaptation 1
sociocultural adjustment 1 4
acculturation and enculturation 1
acculturation strategies 2 2
acculturative adjustment 1
americanization 1
cross-cultural adaptation 1
cross-cultural adjustment 1
cultural adaptation 1
cultural identification 1
cultural orientation 1
cultural transition 1
cultural transmission 1
culturing 1
economic integration 1
immigation success 1
intercultural adjustment 1
linguistic acculturation 1
mental health 1
national identity 1
psychological and sociocultural adjustment 1
psychological flourishing 1

Data Collection Methods

We then compare the data collection methods most frequently used by the authors in the different disciplines (see Table 54 and Figure 29).

# Frequency Table of data collection type by field
empiricalDataDisciplines %>%
  dplyr::select(discipline02, Method) %>%
  mutate(Method = tolower(Method)) %>%
  group_by(discipline02, Method) %>%
  summarise(Frequency=n()) %>%
  spread(discipline02, Frequency) %>%
  arrange(desc(Psychology)) %>%
  mutate_if(is.numeric, funs(replace_na(as.character(.), ""))) %>%
  dplyr::select(Method, Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  kbl(., caption = "Method used per discipline",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 54: Method used per discipline
Method Psychology Medicine, Nursing, & Health Social Sciences (miscellaneous) Multidisciplinary / Crossdisciplinary
quantitative 114 126 38 96
mixed method 3 10 4 5
qualitative 3 8 3 1
review 2 
# compare collection methods between disciplines
ggstatsplot::ggbarstats(
  data = empiricalDataDisciplines,
  x = Method,
  y = discipline02,
  #sampling.plan = "jointMulti",
  title = "Data Collection Method by Discipline",
  xlab = "Discipline",
  package = "wesanderson",
  palette = "Darjeeling2",
  ggtheme = ggthemes::theme_tufte(base_size = 12),
  ggplot.component = list(scale_x_discrete(guide = guide_axis(n.dodge = 2))),
  ggstatsplot.layer = FALSE,
  label.args = list(check_overlap = TRUE, alpha = 1, fill = "white"),
  messages = FALSE
)
Stacked bar chart of data collection method by discipline.

Figure 29: Stacked bar chart of data collection method by discipline.

Measures

We also assessed the use of different validated and novel measures used by authors in the various disciplines (see Table 55). Note, again, that a majority of the measurements are not previously standardized and are not shared across articles and disciplines.

# Frequency Table of acculturation measures used by field
empiricalDataDisciplines %>%
  dplyr::select(discipline02, MeasureDefinition) %>%
  mutate(MeasureDefinition = tolower(MeasureDefinition)) %>%
  group_by(discipline02, Measure = MeasureDefinition) %>%
  summarise(Frequency=n()) %>%
  spread(discipline02, Frequency) %>%
  arrange(desc(Psychology)) %>%
  mutate_if(is.numeric, funs(replace_na(as.character(.), ""))) %>%
  dplyr::select(Measure, Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  kbl(., caption = "Measure used per discipline",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
   scroll_box(width = "110%", height = "500px")
Table 55: Measure used per discipline
Measure Psychology Medicine, Nursing, & Health Social Sciences (miscellaneous) Multidisciplinary / Crossdisciplinary
vancouver index of acculturation (via; ryder et al., 2000) 5 1 2 4
abbreviated multidimensional acculturation scale 3 1
acculturation attitudes (arends-tóth & van de vijver, 2007) 3 1
bicultural involvement questionnaire 3 1
brief acculturation rating scale for mexican americans–ii (arsma ii- sf) 3 2 1
language, identity, and behavioral acculturation scale 3 1 6
relative acculturation extended model scale (real and ideal acculturation strategies and attitudes [raem]) 3 2
vancouver index of acculturation 3 3 3
acculturation attitudes (icsey) 2
acculturation orientation/acculturation strategy 2 3 1 2
acculturation strategies scale (modified; kosic, 1998) 2
assimilation scale (cernovsky, 1990) 2
emotional acculturation 2
identity subscale from language, identity, and behavioral acculturation scale 2
abbreviated multidimensional acculturation scale (amas) 1 1
abbreviated multidimensional acculturation scale (amas); multigroup ethnic identity measure (meim) 1
abbreviated multidimensional acculturation scale (amas); social connectedness in mainstream society (scmn) scale; social connectedness in the ethnic community (sceth) scale 1
acculturation attitude scale (aas-16); sociocultural adjustment scale - revised (scas-r) 1
acculturation attitude scale (sam, 1995) 1 1
acculturation attitudes 1
acculturation attitudes (icsey) [assimilation subscale] 1
acculturation attitudes scale 1 2
acculturation attitudes scale; behavioral adaptation scale 1
acculturation index (ai; ward & kennedy, 1994) 1
acculturation intergenerational conflict scale (aic) 1
acculturation orientation (based on horenczyk, 1996, 2000) 1
acculturation orientation (demes, & geeraert, 2014) 1
acculturation preference [unclear], intergroup contact [unclear] 1
acculturation preferences 1
acculturation preferences [own] 1
acculturation rating scale for mexican-americans - revised (arsma-ii) 1 5 1 1
acculturation scale for vietnamese adolescents (asva) 1
acculturative stress, subjective well-being, flourishing 1
adapted acculturation scale for vietnamese adolescents (asva) 1
adapted cultual adaptation korean elderly 1
adapted soviet acculturation measure (sam-ii) 1
adopted language, identity, and behavioral acculturation scale 1 1
adult acculturation and resiliency scale 1
anticipated socio-cultural adaptation scale 1
asian american multidimensional acculturation scale (aamas; chung, kim, & abreu, 2004) 1 1
assimilation attitude [own] 1
attitudes towards minority culture maintenance and inter-ethnic contact scale 1
behavioral and psychological acculturation 1
bicultural involvement and adjustment scale 1
bicultural stress scale 1
bidimensional acculturation scale for hispanics 1 2
brief sense of community scale (bscs-8); indicators of acculturation domains (raem) 1
cultural identification [own], adapted vancouver index of acculturation (via; ryder et al., 2000) 1
cultural identity, feeling at home, well-being, perception of host society’s attitudes and policies toward them 1
cultural life style inventory 1
cultural maintenance and adoptation 1
demands of immigration scale 1
dutch psychological acculturation scale, language use [own] 1
emotional acculturation, modified vancouver index of acculturation 1
emotional fit with the typical majority patterns of emotion [own] 1
ethnic identification, language proficiency 1
ethnic identity [own] 1
ethnic identity questionnaire 1
european-american values scale for asian-americans—revised (eavs-aa-r); asian-american values scale—revised (aavs-r) 1
general acculturation attitudes 1
ghanaian acculturation survey (gas) 1
host community acculturation scale & immigrant acculturation scale 1
identification [own] 1 1 1
identification; identity; behavior; islamic beliefs; social network; cultural norms; language use, choice, and preference; attiudes towards turkish language 1
identitiy [own] 1
identity style inventory cultural transition + immigrant identity commitment scale 1
immigrant identity questionnaire (iiq) 1
integration well-being scale (amit, litwin) 1
iranian acculturation scale 1 1
japanese american acculturation scale 1
language proficiency 1 1 1
language proficiency (icsey); cultural identity (icsey); acculturation behaviors - peer contacts & activities (icsey) 1
language proficiency [own] 1 2
language use [own] 1 10 1 2
mace’s (2004) acculturation index 1
minority and majority group contact (sim) 1
modified acculturation strategies and attitudes questionnaire, immigrants’ version 1
multidimensional acculturative stress inventory 1
multidimensional measure of cultural identity scales for latinos and latinas 1
multigroup ethnic identity measure 1 1
multigroup ethnic identity measure–revised 1
mutual intercultural relations in plural societies - acculturation attitudes scale 1 1
number of initial pleasant experiences, percentage of initial experiences which were unpleasant, number of’ problems encountered upon arrival, satisfaction with friends, satisfaction with job, satisfaction with school, satisfaction with recreation, task competence, interpersonal comfort, recent symptoms, chronic symptoms, cultural competence (english vocabulary, number series, knowledge of australian slang, knowledge of australian culture - geography, history, politics, and celebrities) 1
participation and identification 1
perez ambiguous loss of homeland scale (palh) 1
preferred and perceived acculturation strategies 1
psychological acculturation scale (tropp et al., 1999) 1 1
psychological adaptation, socio-cultural adjustment scale, economic adaptation 1
self-esteem, life satisfaction, subjective happiness, depression (reversed), anxiety (reversed), loneliness (reversed) 1
social integration in the community scale (from pcsq) 1
social participation [language use - own]; vancouver index of acculturation (via; ryder et al., 2000) 1
socio-cultural adaptation scale (29-item) 1
sociocultural adaptation scale (lsype) 1
stephenson multigroup acculturation scale 1 4
strategies of acculturation scale 1
suinn-lew asian self identity acculturation scale 1 2 1
symptom checklist-90-revised scale, satisfaction with live scale, riverside acculturation stress inventory, bicultural identity integration scale 1
vancouver index of acculturation (via; ryder et al., 2000); self-esteem, subjective well-being; life satisfaction 1
2 measures - attitudes (intrinsic) & extrinsic cultural traits 1
abbreviated acculturation modes for koreans (lee, sobal, and frongillo, 2003) 1
abridged general ethnicity questionnaire—american and chinese version 1
abridged general ethnicity questionnaire—american version (geqa) 1
acceptance of american cultural values; affinity for korean cultural identity 1
accultuation orientation (arends-tóth & van de vijver, 2004) 1
acculturation attitude scale (berry, 1989) 1
acculturation attitude scale (kim, 1988) 1
acculturation in australia 1
acculturation index 1
acculturation index (ai; ward & kennedy, 1994); identification 1
acculturation index (nesarc) 1
acculturation indicators (needham, et al., 2017) 1
acculturation rating scale for mexican americans-ii 3
acculturation rating scale for mexican americans-ii (language sub-scale) 1
acculturation rating scale for mexican-americans - revised (arsma-ii); mexican american cultural values scale (macvs) 1
acculturation scale (acc) 1
acculturation scale (cheung, 1995) 1
acculturation scale for southeast asians 1
acculturation scale for vietnamese adolescents (asva); multigroup ethnic identity measure 1
acculturation strategies (mancini, & bottura, 2014); acculturation attitudes (mancini, & bottura, 2014) 1
acculturation, habits and interests multicultural scale for adolescents (ahimsa) 2
acculturative dissonance (ad) 1
adapted acculturation rating scale for mexican-americans-ii (arsma-ii) (schaefer, et al., 2009) 1
adapted biculturalism scale (nagaraj, vyas, mcdonnell, dipietro) 1
adapted bidimensional acculturation scale for hispanics 1 1
adapted ethnic/cultural identity dimensions scale (eid scale) 1
adapted general ethnicity questionnaire 1
adapted hazuda scale 1
adapted measurement of acculturation strategies for people of african descent (maspad) - uk 1
adapted short acculturation scale for hispanics (marin, sabogal, marin, otero-sabogal, & perez-stable, 1987) 1
adjustment experience [own] 1
adjustments to u.s. national culture 1
affective adjustment 1
african american acculturation scale revised (klonoff & landrine, 2000) 1
american and puerto rican cultural involvement scales 1
asian indian dietary acculturation measure (aidam) 1
assimilation to french culture 1
attitudes toward homosexuality (ess) 1
attitudes towards majority culture, identification 1
background information, heritage culture maintenance, mobility strategies and personal ability, perceived justice, self-perceptions, community contacts 1
behavior subscale from language, identity, and behavioral acculturation scale 1
behavioral acculturation scale 1 1
belonging, cultural memory (identity and difference cultural), empathy and reciprocity, perfection of speech, adaption 1
bicultural identification 1
bicultural involvement questionnaire; acculturation rating scale for mexican-americans - revised (arsma-ii) 1
bicultural youth acculturation questionnaire 1
bidimensional acculturation scale 1 1
bidimensional acculturation scale for marriage-based immigrants (basmbi) 1
bidimensional model of acculturation for korean american older adults 1
brief acculturation rating scale for mexican americans 1
brief symptoms inventory–18 (bsi–18) 1
chis acculturation score 2
coging strategies, contacts, friendship, belonging 1
contentment and gratitude, a strong family concept, the concept of face, an urge for knowledge, a sense of crisis, faith in life, and the identity of a marginal man [own] 1
cultual adaptation korean elderly 2
cultural adaptation (jeong, & kim, 2016) 1
cultural adherence 1
cultural familiarity [own] 1
cultural identification 1
cultural orientation, interaction 1
cultural practice, and social networks 1
culture-transmission motive; cultural identity and biculturality 1
degree of difficulty in dealing with practical, social, and interpersonal communication problems 1
east asian acculturation measure 1 1
economic success, psychological well-being (sense of belonging, life satisfaction) 1
educational achievement [own], job prestige [own], income [own], generation [own] 1
employed [own] 1
employment status [own], diverse friends network outside ethnic community [own], association membership [own], ability to overcome difficulties [own], desire to remain [own] 1
ethnic label [own] 1
ethnic/cultural identity salience questionnaire 1
ethnocultural identity behavior index 1
expatriate cross-cultural adjustment scale 1
food similarity index (fsi) 1
food similarity index (fsi), mexican food [own] 1
frankfurt acculturation scale 1
frankfurt acculturation scale (frakk) 1
general acculturation index (gai) 1
geriatric depression scale - 30 1
hazuda scale [language use only] 1
host communication competence, host interpersonal communication, psychological health, functional fitness 1
identity style inventory cultural transition 1
immigrant acculturation scale 3 1
immigration policy lab (ipl) integration index 1
integration effort 1
intention to return home, agreement with native values & political identification 1
interactions [own] 1
intercultural adjustment potential scale (icaps) 1
interethnic contact 1
interview language 1
inventory of student adjustment, sociocultural adjustment scale, australian cultural knowledge (ack) questionnaire, cultural identiy subscale of collective self-esteem scale 1
italian-canadian acculturation scale (kim, laroche, & tomiuk, 2001) 1
korean version of the acculturation rating scale for mexican americans-ii 1
language 1
language acquisition, dual national identity, realised expectations 1
language barriers, and getting used to life in australia 1
language difficulties, social support, economic situation, prejudice 1
language fluency, prior employment 1
language proficiency [own], ethnic identification 1
language proficiency [own], identification [own], acculturation desire [own], marriage values [own], children ideals [own] 1
language proficiency [own], language use [own] 1 1
language proficiency [own], language use [own], media consumption [own] 1
language proficiency [own], time of residence [own], media consumption [own], national visitors [own] 1
language proficiency [own]; language use [own] 2
language proficiency and length of residence 1
language proficiency, and cultural identity 1
language proficiency, language use, preferred language, food preferences, ethnicity of social contacts 1
language proficiency, languages use, preferred languages for media, food preference, ethnicity of close friends 1
language proficiency, length of residency, utilization of societal resources etc. 1
language proficiency, personal preference, social affiliation, lifestyle, and attachment to values and traditions 1
language proficiency, social support, cultural distance, contact 1
language skills [own], work status [own], ethnic identity [own] 1
language use 1
language use [own], community event participation [own] 1
language use [own], language preference media consumption [own] 1
language use, length of stay, friendship composition, generation, percentage of native-born persons in the respondent’s neighborhood, and percentage of non-coethnics in the respondent’s neighborhood [own] 1
language use, media behavior, social customs, social contacts, cultural identity, generational status 1
lanuage preference [own] 1
learning language, navigation, regulations 1
leisure participation 1
level of acculturation to korean life 1
life satisfaction index a 1
locus of control, giving and receiving, individualism-collectivism, 1
lowlands acculturation scale 3
marital assimilation [own] 1 1
mental health continuum short form (mhc-sf) 1
migrant integration indexes (lindstorm, 2019) 1
modified american and puerto rican cultural involvement scales 1
modified bicultural involvement questionnaire 1
modified global acculturation index 1
modified lowlands acculturation scale 1
modified suinn-lew asian self-identity acculturation scale 1
multicultural acculturation questionnaire 1
multidimensional acculturative stress scale 2
multigroup ethnic identity measure revised 1 1
multigroup ethnic identity measure; acculturation attitudes (arends-tóth & van de vijver, 2007) 1
national identification [own] 1
national identity 1
pooyan behavioral scale 1
primary language [own], cultural fluency [own], cultural orientation [own] 1
proportion of native friends, feelings of inclusion, satisfaction with employment, accommodation, and life 1
proxy acculturation scale (pas-3 and pas-4) 5
proxy acculturation scale (pas-3) 2
psychological acculturation scale 1
psychological acculturation scale, massachusetts hispanic elderly study acculturation scale 1
psychological well-being questionnaire, emotional state questionnaire, satisfaction level questionnaire, 90-symptom checklist 1
reactions to assimilation expectations 1
relative acculturation extended model scale (real and ideal acculturation strategies and attitudes) 1
revised haitian acculturation scale 1
satisfaction with migration life scale 1
self-administered questionnaire of acculturation 1
self-reported life satisfaction 1
sense of belinging [own], language use [own], social network [own] 1
short acculturation scale (marin, sabogal, marin, otero-sabogal, & perez-stable, 1987) 1
short acculturation scale for filipino americans 1
short acculturation scale for hispanics 1
short acculturation scale for hispanics (5 item version) 1
short acculturation scale for hispanics (language subscale) 1
short acculturation scale for hispanics (marin, sabogal, marin, otero-sabogal, & perez-stable, 1987) 7
social adaptation structured interview, social adjustment questionnaire 1
social contacts [own] 1
social integration 1
social integration index (dalgard, & thapa, 2007) 1
social markers of acculturation 1
social readjustment rating scale 1
social support list - interactions scale 1
socio-cultural adaptation scale 1
sociocultural adaptation - school adjustment (icsey); psychological adaptation - life satisfaction (icsey); psychological adaptation - self esteem (icsey); psychological adaptation - psychological problems (icsey); sociocultural adaptation - behavior problems (icsey) 1
sociocultural difficulties (unclear origin) 1
speaking english at home [own] 1 1
spheres of sociocultural adjustment 1
structured interview 1
suinn-lew asian self-identity accultururation scale (sl-asia) 2
survey language [own] 1
time in country, maintanance hertiage culture, adaptation host culture, social support migrant community, social support host community 1
trust [own], civic engagment measure 1
van manen’s approach to phenomenology 1
virgin island acculturation scale (tull, ambrose, & chambers, 2003) 1
working hours [own] 1
1

Study Focus

For the empirical works we also coded the main focus of the papers in each of the disciplines. The wordclouds of the topics within each discipline illustrate that the different disciplines also focus on different topics related to cultural adaptation (see Figure 30).

# unique domains already calculated

# dataframe to compile domain frequency for Review Scales
PsychDomainFreq <- data.frame(UniqueDomains)

# count domain frequency in dt.Scales.Included
for (i in 1:nrow(UniqueDomains)) {
  PsychDomainFreq$Psychology[i] <- length(grep(UniqueDomains$Domain[i], 
                                               empiricalDataDisciplines$domainPaper[empiricalDataDisciplines$discipline02=="Psychology"], 
                                               value = T))
  PsychDomainFreq$`Medicine, Nursing, & Health`[i] <- length(grep(UniqueDomains$Domain[i], 
                                               empiricalDataDisciplines$domainPaper[empiricalDataDisciplines$discipline02=="Medicine, Nursing, & Health"], 
                                               value = T))
  PsychDomainFreq$`Social Sciences (miscellaneous)`[i] <- length(grep(UniqueDomains$Domain[i], 
                                               empiricalDataDisciplines$domainPaper[empiricalDataDisciplines$discipline02=="Social Sciences (miscellaneous)"], 
                                               value = T))
  PsychDomainFreq$`Multidisciplinary / Crossdisciplinary`[i] <- length(grep(UniqueDomains$Domain[i], 
                                               empiricalDataDisciplines$domainPaper[empiricalDataDisciplines$discipline02=="Multidisciplinary / Crossdisciplinary"], 
                                               value = T))
}

library(ggwordcloud)
set.seed(7) # for reproducibility 
PsychDomainFreq %>%
  dplyr::select(Domain,
                Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  gather(discipline02, Frequency, -Domain) %>%
  filter(Frequency>0) %>%
ggplot( ., aes(label = Domain, size = Frequency, color = Frequency)) +
  geom_text_wordcloud_area(shape = 'circle', rm_outside = TRUE, eccentricity = 1) +
  scale_size(range = c(2,10)) +
  theme_minimal() +
  facet_wrap(~discipline02, ncol = 1)
Wordcloud of the article foci in the empirical results.

Figure 30: Wordcloud of the article foci in the empirical results.

Additionally, we coded where in the model the acculturation measure was placed most frequently in each of the disciplines (see Table 56).

empiricalDataDisciplines %>%
  dplyr::select(discipline02, VariableType) %>%
  group_by(discipline02, `Variable Type` = VariableType) %>%
  summarise(Frequency=n()) %>%
  spread(discipline02, Frequency) %>%
  filter(!is.na(`Variable Type`)) %>%
  arrange(desc(Psychology)) %>%
  mutate_if(is.numeric, funs(replace_na(as.character(.), ""))) %>%
  dplyr::select(`Variable Type`, Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  kbl(., caption = "Variable Type of Acculturation Measure per discipline",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 56: Variable Type of Acculturation Measure per discipline
Variable Type Psychology Medicine, Nursing, & Health Social Sciences (miscellaneous) Multidisciplinary / Crossdisciplinary
Predictor 58 108 19 43
Dependent 40 19 17 39
Predictor & Dependent 12 3 3 5
Correlation 6 9 3 5
Mediator 4 1 1 4
Moderator 1 1 3
N/A 1 2 1
Control 1
selection criterion 1 1
validation; Predictor 1

Majority Inclusion

Only a minority of studies included data on the acculturation process from the majority and migrant perspective jointly. And there is a stark contrast between the disciplines in how many of the total papers included the majority group (see Table 57).

empiricalDataDisciplines %>%
  dplyr::select(discipline02, IncludesMajority) %>%
  mutate(`Includes Majority` = recode_factor(.$IncludesMajority, `0` = "no", `1` = "yes")) %>%
  group_by(discipline02, `Includes Majority`) %>%
  summarise(Frequency=n()) %>%
  spread(discipline02, Frequency) %>%
  arrange(desc(Psychology)) %>%
  filter(!is.na(`Includes Majority`)) %>%
  mutate_if(is.numeric, funs(replace_na(as.character(.), ""))) %>%
  dplyr::select(`Includes Majority`, 
                Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  kbl(., caption = "Includes Majority per discipline",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 57: Includes Majority per discipline
Includes Majority Psychology Medicine, Nursing, & Health Social Sciences (miscellaneous) Multidisciplinary / Crossdisciplinary
no 104 124 40 81
yes 18 20 4 21

Year of Publication

As with the overall developments we also assessed the publication developments within the individual disciplines. Article ublication developments over time should indicate empirical interest in the topic of acculturation in the disciplines over time. We offer a global, developmental overview of the fields in Figure 31, as well as a visualization that further distinguishes between data collection types within the fields (see Figure 32).

# create super short labels for grid labels
discipline_labs_shrt <- c(
  'Psychology'="Psych.",
  'Medicine, Nursing, & Health'="Med.",
  'Social Sciences (miscellaneous)'="SocSci.",
  'Multidisciplinary / Crossdisciplinary'="Multi."
)

# create short labels for grid labels
discipline_labs_lng <- c(
  'Psychology'="Psychology",
  'Medicine, Nursing, & Health'="Medicine",
  'Social Sciences (miscellaneous)'="Social Sci.",
  'Multidisciplinary / Crossdisciplinary'="Multidiscipl."
)

# plot number of publications per year for each field
ggplot(empiricalDataDisciplines, aes(x=year)) + 
  geom_histogram(binwidth = .9, fill = "grey14")+
  facet_grid(rows = vars(discipline02), labeller = as_labeller(discipline_labs_shrt)) +
  ylab("Number of Results") +
  xlab("Year") +
  ggtitle("Publication Year of Results")+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Histogram of the publications per year for all fields.

Figure 31: Histogram of the publications per year for all fields. 

# plot type of data collected per year for each field
ggplot(empiricalDataDisciplines, aes(x=year, color = Method)) + 
  #geom_density(aes(y = ..count..)) +
  geom_line(stat='count') +
  geom_point(stat='count', size = .9) +
  facet_grid(rows = vars(discipline02), labeller = as_labeller(discipline_labs_shrt)) +
  ylab("Number of Results")+
  xlab("Year") +
  ggtitle("Data Collection Method by Discipline over Time")+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="bottom", legend.title = element_blank())
Line plot of the yearly publication frequency for by data collection method.

Figure 32: Line plot of the yearly publication frequency for by data collection method.

Experience

Again, a major focus of our coding was the role of affect, behavior, cognition, and desires in the each of the disciplines. Figure 33 shows how often each of the four dimensions was coded in each of the disciplines. Note that in order better compare the disciplines we output percentages in most of the figures of this section.

# Plot aspect frequencies by field
empiricalDataDisciplines %>%
  dplyr::select(discipline02, Affect=AffectFinal, Behavior=BehaviorFinal, Cognition=CognitionFinal, Desire=DesireFinal) %>%
  gather(ABCD, Frequency, -discipline02) %>%
  group_by(discipline02, ABCD) %>%
  summarise_each(funs(sum(., na.rm = TRUE))) %>%
  ungroup() %>%
  group_by(discipline02) %>%
  mutate(Percentage = Frequency/sum(Frequency)*100) %>%
  ungroup() %>%
  ggplot(data = ., aes(x=reorder(ABCD, Percentage), y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    aes(label = round(Percentage,0), y = Percentage - 5),
    position = position_dodge(0.9),
    vjust = 0,
    color = "white") +
  facet_grid(cols = vars(discipline02), labeller = as_labeller(discipline_labs_lng)) +
  xlab("Dimension") +
  ylab("Percentage [in %]") +
  coord_flip()+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Bar graph of the counts for each of the dimensions across all included empirical works.

Figure 33: Bar graph of the counts for each of the dimensions across all included empirical works.

Again, we also plot how often each of the dimensions were measured together. A bar graph of the compound frequencies in each discipline is shown Figure 34. Interestingly, there are certain dimensions and dimension combinations that are missing in some of the disciplines. There were, for example, only two articles in the psychological discipline that only measured behavioral acculturation a dimension that was important in all other disciplines.

# plot frequencies of aspect combinations by field
empiricalDataDisciplines %>%
  dplyr::select(discipline02, Affect=AffectFinal, Behavior=BehaviorFinal, Cognition=CognitionFinal, Desire=DesireFinal) %>%
  group_by(discipline02, Affect, Behavior, Cognition, Desire) %>%
  summarise(Frequency = n()) %>%
  ungroup() %>%
  group_by(discipline02) %>%
  mutate(Percentage = Frequency/sum(Frequency)*100) %>%
  ungroup() %>%
  mutate_at(.vars = c("Affect", "Behavior", "Cognition", "Desire"), funs(deparse(substitute(.))[.])) %>%
  unite("Dimension Combination", c(Affect, Behavior, Cognition, Desire), sep = ", ", na.rm = T) %>%
  ggplot(., aes(x=reorder(`Dimension Combination`, Percentage), y=Percentage)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    aes(label = round(Percentage,0)),
    position=position_stack(vjust=0.5),
    color = "white",
    size = 3,
    vjust = 0.5) +
  facet_wrap(vars(discipline02), nrow = 2, labeller = as_labeller(discipline_labs_lng)) +
  ggtitle("Compound Dimension Percentages") +
  ylab("Percentage [in %]") +
  xlab("Dimension Combination") +
  coord_flip()+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Bar graph of the counts for each of the dimension combinations.

Figure 34: Bar graph of the counts for each of the dimension combinations.

Context

Culture

Similar to our overall overview we also assessed whether different disciplines relied on different migrant regions or host countries. Again, we coded both the migrants’ country of origin as well as the country of the receiving society, in which the study was conducted for each discipline (see Table 58).

# Frequencies of host country focus
empiricalFieldHostFreq <- empiricalDataDisciplines %>%
  dplyr::select(HostCountry, discipline02) %>%
  mutate(country = strsplit(as.character(HostCountry), ", ")) %>% 
  unnest(country) %>%
  group_by(discipline02, country) %>%
  summarise(Host = n()) %>%
  arrange(-Host)
  
# Frequencies of origin country focus
empiricalFieldOriginFreq <- empiricalDataDisciplines %>%
  dplyr::select(OriginCountry, discipline02) %>%
  mutate(country = strsplit(as.character(OriginCountry), ", ")) %>% 
  unnest(country) %>%
  group_by(discipline02, country) %>%
  summarise(Origin = n()) %>%
  arrange(-Origin)

# Frequencies of host country focus in wide format
empiricalFieldCountryFreqWide <- merge(empiricalFieldHostFreq, empiricalFieldOriginFreq, by = c("discipline02", "country"), all = TRUE) %>%
  mutate_if(is.numeric, funs(replace_na(., 0)))

# Frequencies of host country focus in long format
empiricalFieldCountryFreqLong <- empiricalFieldCountryFreqWide %>%
  melt(., id = c("discipline02", "country"), value.name = "Frequency")
# Create table of host and origin countries by field
empiricalFieldCountryFreqWide %>%
  filter(!is.na(country)) %>%
  arrange(discipline02, desc(Host), desc(Origin)) %>%
  dplyr::select(-discipline02) %>%
  kbl(., caption = "Considered Cultures per discipline",
      format = "html",
      align = c("l", "c", "c")) %>%
  pack_rows(index = table(empiricalFieldCountryFreqWide %>% 
                            filter(!is.na(country)) %>%
                            dplyr::select(discipline02))) %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria") %>%
   scroll_box(width = "100%", height = "500px")
Table 58: Considered Cultures per discipline
country Host Origin
Medicine, Nursing, & Health
United States 100 0
Netherlands 7 0
Australia 6 0
Canada 5 0
Korea 4 15
Germany 4 0
Greece 4 0
Israel 2 0
Norway 2 0
Taiwan 2 0
Britain 1 1
Finnland 1 0
Hong Kong 1 0
Italy 1 0
New Zealand 1 0
Spain 1 0
Sweden 1 0
United Kingdom 1 0
Virgin Islands 1 0
any 0 23
LatinX 0 23
China 0 15
Mexico 0 15
Vietnam 0 9
Asia 0 8
Turkey 0 5
Cambodia 0 3
Somalia 0 3
former Soviet Union 0 2
Hispanic 0 2
India 0 2
Iraq 0 2
Laos 0 2
Pakistan 0 2
Philippines 0 2
Puerto Rico 0 2
South Asia 0 2
Southeast Asia 0 2
Afghanistan 0 1
African Cribbean 0 1
Albania 0 1
Arab 0 1
Asian 0 1
Asian Indian 0 1
Australasia 0 1
Bosnia 0 1
Ecuador 0 1
Eritrea 0 1
Finland 0 1
former Yugoslavia 0 1
Ghana 0 1
Haiti 0 1
Hispanics 0 1
Indonesia 0 1
Iran 0 1
Japan 0 1
Jordan 0 1
Kurdistan 0 1
Lebanon 0 1
Marocco 0 1
Morocco 0 1
Muslims 0 1
Nepal 0 1
Nigeria 0 1
Palestine 0 1
Phillipines 0 1
Pontic Greeks 0 1
Romania 0 1
Russia 0 1
Surinam 0 1
Syria 0 1
Multidisciplinary / Crossdisciplinary
United States 42 0
Canada 13 0
Australia 8 0
Spain 6 0
any 3 24
Israel 3 1
Italy 3 1
France 3 0
Netherlands 3 0
Britain 2 0
Germany 2 0
Greece 2 0
New Zealand 2 0
Norway 2 0
Thailand 2 0
Russia 1 6
Turkey 1 3
Austria 1 0
Finland 1 0
Hong Kong 1 0
Israel; Germany 1 0
Northern Ireland 1 0
Singapore 1 0
South Korea 1 0
Switzerland 1 0
United Kingdom 1 0
LatinX 0 11
China 0 8
Asia 0 5
Mexico 0 4
Vietnam 0 4
Bosnia 0 3
Korea 0 3
Marocco 0 3
Somalia 0 3
Ecuador 0 2
former Soviet Union 0 2
Hmong 0 2
India 0 2
Iran 0 2
North Africa 0 2
Pakistan 0 2
Sub-Saharan Africa 0 2
“visibly different” immigrants 0 1
Africa 0 1
Arab 0 1
Brazil 0 1
Cambodia 0 1
Central America 0 1
Colombia 0 1
Cuba 0 1
Dominican Republic 0 1
East Asian 0 1
Former Soviet Union 0 1
Hungary 0 1
Japan 0 1
Laos 0 1
Malaysia 0 1
Many 0 1
Middle East 0 1
multiple 0 1
Myanmar 0 1
Nigeria 0 1
non-western 0 1
North Korea 0 1
Poland 0 1
Portugal 0 1
Senegal 0 1
South Asia 0 1
Soviet Union 0 1
spanish speaking 0 1
Sri Lanka 0 1
Syria 0 1
Psychology
United States 36 0
Australia 12 0
Canada 11 0
Germany 8 0
Israel 7 0
Italy 7 0
Netherlands 7 0
Spain 7 0
Belgium 5 0
New Zealand 4 1
Switzerland 4 0
United Kingdom 4 0
Russia 3 5
Hong Kong 2 1
Portugal 2 1
Finland 2 0
France 2 0
any 1 31
N/A 1 1
Britain 1 0
Canda 1 0
Finnland 1 0
Greece 1 0
LatinX 0 11
China 0 10
former Soviet Union 0 9
Korea 0 7
Marocco 0 5
Turkey 0 5
Ethiopia 0 3
Japan 0 3
Poland 0 3
Romania 0 3
Vietnam 0 3
Croatia 0 2
Czechoslovacia 0 2
Ghana 0 2
India 0 2
Iran 0 2
Mexico 0 2
North Africa 0 2
Sri Lanka 0 2
Sudan 0 2
Syria 0 2
Taiwan 0 2
Africa 0 1
Albania 0 1
Cambodia 0 1
Caribbeans 0 1
Congo 0 1
Dominican Republic 0 1
Ecuador 0 1
Ecuadorian 0 1
Eritrea 0 1
Former Soviet Union Jews 0 1
Hispanic 0 1
Hispanics 0 1
Iraq 0 1
Kazakhstan 0 1
Kyrgyzstan 0 1
Latin America 0 1
Malaysia 0 1
Moroccan 0 1
Myanmar 0 1
Pakistani 0 1
Phillipines 0 1
Romanian 0 1
Somalia 0 1
South Asia 0 1
South Korea 0 1
Soviet Union 0 1
Sub-Saharan Africa 0 1
Tajikistan 0 1
Tunesia 0 1
Turkmenistan 0 1
Uzbekistan 0 1
Yugoslavia 0 1
Social Sciences (miscellaneous)
United States 25 0
Canada 5 0
Hong Kong 2 1
Israel 2 1
Australia 2 0
Europe 2 0
Germany 2 0
China 1 6
Chile 1 0
Netherlands 1 0
New Zealand 1 0
Saudi Arabia 1 0
South Africa 1 0
Sweden 1 0
UK 1 0
any 0 15
LatinX 0 5
Korea 0 3
Mexico 0 2
Afro-Colombian 0 1
Asia 0 1
former Soviet Union 0 1
Former Soviet Union 0 1
Hmong 0 1
India 0 1
Jamaica 0 1
Japan 0 1
Muslims 0 1
Poland 0 1
Russia 0 1
Somalia 0 1
Southeast Asia 0 1
Sri Lanka 0 1
Turkey 0 1
West Africa 0 1

Individual

To gain a deeper understanding of the study setups in the empirical studies we coded the type of sample recruited within each of the disciplines (see Figure 35).

# tally different samples per field
empiricalDataDisciplines %>%
  dplyr::select(discipline02, Sample) %>%
  group_by(discipline02, Sample) %>%
  summarise(Frequency = n()) %>%
  drop_na() %>%
  ggplot(data = ., aes(x=reorder(Sample, Frequency), y=Frequency)) +
  geom_bar(stat="identity", fill="grey14") +
  geom_text(
    aes(label = round(Frequency,0), y = Frequency + 6),
    position = position_dodge(0.9),
    hjust = 0.5) +
  facet_grid(cols = vars(discipline02), labeller = as_labeller(discipline_labs_lng)) +
  xlab("Sample") +
  ylab("Frequency") +
  ylim(0,50) +
  coord_flip()+
  theme_Publication()+
  theme(strip.background =element_rect(fill="grey14", color="grey14"),
        strip.text = element_text(colour = 'white', face="bold"),
        legend.position="none")
Bar graph of the study samples used in the empirical studies. Note: general = any migrant from specified country (no targeting).

Figure 35: Bar graph of the study samples used in the empirical studies. Note: general = any migrant from specified country (no targeting).

Again the category general refers to a sampling strategy in which any consenting adult would be able to participate in the study.

Situation

We again coded which life domains the authors referred to, either as part of subscale labels, factor labels, explicit commentary of the authors, or clear question wordings to gain an understanding of the situational focus the authors chose. However, we did not code the theoretical situational life domains because such an undertaking would be beyond the scope of this paper.

Process

We also assessed which time point in the migration process was targeted within each discipline. Table 59 showcases that an overwhelming majority of studies targets migrants only after they had left their country of origin.

empiricalDataDisciplines %>%
  dplyr::select(discipline02, MigrationTime) %>%
  group_by(discipline02, MigrationTime) %>%
  summarise(Frequency=n()) %>%
  spread(discipline02, Frequency) %>%
  arrange(desc(Psychology)) %>%
  filter(!is.na(MigrationTime)) %>%
  mutate_if(is.numeric, funs(replace_na(as.character(.), ""))) %>%
  dplyr::select(MigrationTime, Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  kbl(., caption = "Migration time per discipline",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 59: Migration time per discipline
MigrationTime Psychology Medicine, Nursing, & Health Social Sciences (miscellaneous) Multidisciplinary / Crossdisciplinary
post 119 140 44 98
pre & post 2 4 3
potential 1 1

Finally, we also coded what kind of analyses the authors conducted with the acculturation measurements in each of the fields (see Table 60).

# table of analyses by field
empiricalDataDisciplines %>%
  dplyr::select(discipline02,MainAnalysis) %>%
  group_by(discipline02, Analysis = MainAnalysis) %>%
  summarise(Frequency=n()) %>%
  spread(discipline02, Frequency) %>%
  filter(!is.na(Analysis)) %>%
  arrange(desc(Psychology)) %>%
  mutate_if(is.numeric, funs(replace_na(as.character(.), ""))) %>%
  dplyr::select(Analysis, Psychology, 
                `Medicine, Nursing, & Health`,
                `Social Sciences (miscellaneous)`,
                `Multidisciplinary / Crossdisciplinary`) %>%
  kbl(., caption = "Analyses used per discipline",
      format = "html") %>%
  kable_classic(full_width = F, 
                lightable_options = "hover", 
                html_font = "Cambria")
Table 60: Analyses used per discipline
Analysis Psychology Medicine, Nursing, & Health Social Sciences (miscellaneous) Multidisciplinary / Crossdisciplinary
regression (incl. PROCESS) 59 99 22 48
SEM 17 4 4 11
mean difference 12 12 6 11
correlation 7 8 3 5
validation 7 7 2 10
longitudinal (dynamic) 6 4 6
descriptive (static) 5 5 1 2
cluster 2 1 3
lagged regression 2 1
dynamic 1
lagged (dynamic) 1 1
N/A 1 2
Social Network Analysis 1
static 1
content analysis 2 2 2
participant selection 1 1
path analysis 1
phenomenological praxis 1

Software Information

The full session information with all relevant system information and all loaded and installed packages is available in the collapsible section below.

System Info
Table 61: R environment session info for reproducibility of results
Setting Value
version R version 4.2.1 (2022-06-23)
os macOS Big Sur … 10.16
system x86_64, darwin17.0
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Amsterdam
date 2023-04-28
pandoc 3.1.1 @ /usr/local/bin/ (via rmarkdown)

Package Info
Table 62: Package info for reproducibility of results
Package Loaded version Date Source
bookdown 0.27 2022-06-14 CRAN (R 4.2.0)
boot 1.3-28 2021-05-03 CRAN (R 4.2.0)
cowplot 1.1.1 2020-12-30 CRAN (R 4.2.0)
data.table 1.14.2 2021-09-27 CRAN (R 4.2.0)
devtools 2.4.3 2021-11-30 CRAN (R 4.2.0)
DiagrammeR 1.0.9 2022-03-05 CRAN (R 4.2.0)
DiagrammeRsvg 0.1 2016-02-04 CRAN (R 4.2.0)
dplyr 1.1.1 2023-03-22 CRAN (R 4.2.0)
DT 0.25 2022-09-12 CRAN (R 4.2.1)
ellipse 0.4.3 2022-05-31 CRAN (R 4.2.0)
flextable 0.7.2 2022-06-12 CRAN (R 4.2.0)
forcats 0.5.1 2021-01-27 CRAN (R 4.2.0)
Formula 1.2-4 2020-10-16 CRAN (R 4.2.0)
GGally 2.1.2 2021-06-21 CRAN (R 4.2.0)
ggplot2 3.3.6 2022-05-03 CRAN (R 4.2.0)
ggrepel 0.9.1 2021-01-15 CRAN (R 4.2.0)
ggstatsplot 0.7.2 2021-04-12 CRAN (R 4.2.1)
ggthemes 4.2.4 2021-01-20 CRAN (R 4.2.0)
ggwordcloud 0.5.0 2019-06-02 CRAN (R 4.2.0)
gridExtra 2.3 2017-09-09 CRAN (R 4.2.0)
gsheet 0.4.5 2020-04-07 CRAN (R 4.2.0)
gsubfn 0.7 2018-03-16 CRAN (R 4.2.0)
haven 2.5.0 2022-04-15 CRAN (R 4.2.0)
Hmisc 4.7-0 2022-04-19 CRAN (R 4.2.0)
hrbrthemes 0.8.0 2020-03-06 CRAN (R 4.2.0)
htmlwidgets 1.5.4 2021-09-08 CRAN (R 4.2.0)
ISOcodes 2022.01.10 2022-01-10 CRAN (R 4.2.0)
janitor 2.1.0 2021-01-05 CRAN (R 4.2.0)
kableExtra 1.3.4 2021-02-20 CRAN (R 4.2.0)
knitr 1.39 2022-04-26 CRAN (R 4.2.0)
lattice 0.20-41 2020-04-02 CRAN (R 4.2.1)
lda 1.4.2 2015-11-22 CRAN (R 4.2.0)
ldatuning 1.0.2 2020-04-21 CRAN (R 4.2.0)
lubridate 1.8.0 2021-10-07 CRAN (R 4.2.0)
mada 0.5.10 2020-05-25 CRAN (R 4.2.0)
manipulateWidget 0.11.1 2021-10-05 CRAN (R 4.2.0)
matrixStats 0.62.0 2022-04-19 CRAN (R 4.2.0)
mvmeta 1.0.3 2019-12-10 CRAN (R 4.2.0)
mvtnorm 1.1-3 2021-10-08 CRAN (R 4.2.0)
naniar 0.6.1 2021-05-14 CRAN (R 4.2.0)
networkD3 0.4 2017-03-18 CRAN (R 4.2.0)
NLP 0.2-1 2020-10-14 CRAN (R 4.2.0)
pals 1.7 2021-04-17 CRAN (R 4.2.0)
pander 0.6.5 2022-03-18 CRAN (R 4.2.0)
patchwork 1.1.2 2022-08-19 CRAN (R 4.2.0)
plotly 4.10.0 2021-10-09 CRAN (R 4.2.0)
proto 1.0.0 2016-10-29 CRAN (R 4.2.0)
psych 2.2.5 2022-05-10 CRAN (R 4.2.0)
RColorBrewer 1.1-3 2022-04-03 CRAN (R 4.2.0)
readxl 1.4.0 2022-03-28 CRAN (R 4.2.0)
remedy 0.1.0 2018-12-03 CRAN (R 4.2.0)
reshape2 1.4.4 2020-04-09 CRAN (R 4.2.0)
rmarkdown 2.14 2022-04-25 CRAN (R 4.2.0)
rmdfiltr 0.1.3 2020-11-25 CRAN (R 4.2.0)
RSQLite 2.2.14 2022-05-07 CRAN (R 4.2.0)
rsvg 2.3.1 2022-04-20 CRAN (R 4.2.0)
sessioninfo 1.2.2 2021-12-06 CRAN (R 4.2.0)
SnowballC 0.7.0 2020-04-01 CRAN (R 4.2.0)
sqldf 0.4-11 2017-06-28 CRAN (R 4.2.0)
stringi 1.7.12 2023-01-11 CRAN (R 4.2.0)
stringr 1.5.0 2022-12-02 CRAN (R 4.2.0)
survival 3.1-12 2020-04-10 CRAN (R 4.2.1)
tibble 3.2.1 2023-03-20 CRAN (R 4.2.0)
tidyr 1.2.1 2022-09-08 CRAN (R 4.2.0)
tm 0.7-8 2020-11-18 CRAN (R 4.2.0)
topicmodels 0.2-12 2021-01-29 CRAN (R 4.2.0)
usethis 2.1.6 2022-05-25 CRAN (R 4.2.0)
visNetwork 2.1.0 2021-09-29 CRAN (R 4.2.0)
webshot2 0.1.0 2022-05-18 CRAN (R 4.2.0)
wordcloud 2.6 2018-08-24 CRAN (R 4.2.0)

Full Session Info (including loaded but unattached packages — for troubleshooting only)

R version 4.2.1 (2022-06-23)

Platform: x86_64-apple-darwin17.0 (64-bit)

locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8

attached base packages:

  • grid
  • stats
  • graphics
  • grDevices
  • datasets
  • utils
  • methods
  • base

other attached packages:

  • devtools(v.2.4.3)
  • usethis(v.2.1.6)
  • ggrepel(v.0.9.1)
  • flextable(v.0.7.2)
  • ldatuning(v.1.0.2)
  • lda(v.1.4.2)
  • SnowballC(v.0.7.0)
  • pals(v.1.7)
  • topicmodels(v.0.2-12)
  • tm(v.0.7-8)
  • NLP(v.0.2-1)
  • DT(v.0.25)
  • boot(v.1.3-28)
  • htmlwidgets(v.1.5.4)
  • manipulateWidget(v.0.11.1)
  • rsvg(v.2.3.1)
  • DiagrammeRsvg(v.0.1)
  • janitor(v.2.1.0)
  • DiagrammeR(v.1.0.9)
  • gsheet(v.0.4.5)
  • lubridate(v.1.8.0)
  • pander(v.0.6.5)
  • ISOcodes(v.2022.01.10)
  • matrixStats(v.0.62.0)
  • naniar(v.0.6.1)
  • mada(v.0.5.10)
  • mvmeta(v.1.0.3)
  • ellipse(v.0.4.3)
  • mvtnorm(v.1.1-3)
  • cowplot(v.1.1.1)
  • patchwork(v.1.1.2)
  • hrbrthemes(v.0.8.0)
  • ggstatsplot(v.0.7.2)
  • GGally(v.2.1.2)
  • networkD3(v.0.4)
  • sqldf(v.0.4-11)
  • RSQLite(v.2.2.14)
  • gsubfn(v.0.7)
  • proto(v.1.0.0)
  • tibble(v.3.2.1)
  • reshape2(v.1.4.4)
  • stringi(v.1.7.12)
  • stringr(v.1.5.0)
  • readxl(v.1.4.0)
  • kableExtra(v.1.3.4)
  • Hmisc(v.4.7-0)
  • Formula(v.1.2-4)
  • survival(v.3.1-12)
  • lattice(v.0.20-41)
  • tidyr(v.1.2.1)
  • dplyr(v.1.1.1)
  • data.table(v.1.14.2)
  • sessioninfo(v.1.2.2)
  • webshot2(v.0.1.0)
  • gridExtra(v.2.3)
  • ggwordcloud(v.0.5.0)
  • visNetwork(v.2.1.0)
  • wordcloud(v.2.6)
  • forcats(v.0.5.1)
  • plotly(v.4.10.0)
  • RColorBrewer(v.1.1-3)
  • haven(v.2.5.0)
  • ggthemes(v.4.2.4)
  • ggplot2(v.3.3.6)
  • psych(v.2.2.5)
  • rmdfiltr(v.0.1.3)
  • bookdown(v.0.27)
  • remedy(v.0.1.0)
  • knitr(v.1.39)
  • rmarkdown(v.2.14)

loaded via a namespace (and not attached):

  • estimability(v.1.4)
  • coda(v.0.19-4)
  • visdat(v.0.5.3)
  • bit64(v.4.0.5)
  • multcomp(v.1.4-19)
  • rpart(v.4.1-15)
  • generics(v.0.1.3)
  • callr(v.3.7.0)
  • TH.data(v.1.1-1)
  • correlation(v.0.8.1)
  • chron(v.2.3-57)
  • bit(v.4.0.4)
  • tzdb(v.0.3.0)
  • webshot(v.0.5.3)
  • xml2(v.1.3.3)
  • httpuv(v.1.6.6)
  • WRS2(v.1.1-4)
  • xfun(v.0.31)
  • hms(v.1.1.2)
  • jquerylib(v.0.1.4)
  • evaluate(v.0.15)
  • promises(v.1.2.0.1)
  • fansi(v.1.0.4)
  • igraph(v.1.3.4)
  • DBI(v.1.1.3)
  • reshape(v.0.8.9)
  • stats4(v.4.2.1)
  • kSamples(v.1.2-9)
  • Rmpfr(v.0.8-9)
  • paletteer(v.1.4.0)
  • purrr(v.1.0.1)
  • ellipsis(v.0.3.2)
  • backports(v.1.4.1)
  • V8(v.4.2.0)
  • insight(v.0.17.1)
  • prismatic(v.1.1.0)
  • ggcorrplot(v.0.1.3)
  • websocket(v.1.4.1)
  • vctrs(v.0.6.1)
  • remotes(v.2.4.2)
  • cachem(v.1.0.6)
  • withr(v.2.5.0)
  • checkmate(v.2.1.0)
  • vroom(v.1.5.7)
  • emmeans(v.1.7.5)
  • prettyunits(v.1.1.1)
  • mnormt(v.2.1.0)
  • svglite(v.2.1.0)
  • cluster(v.2.1.3)
  • lazyeval(v.0.2.2)
  • crayon(v.1.5.1)
  • pkgconfig(v.2.0.3)
  • SuppDists(v.1.1-9.7)
  • slam(v.0.1-50)
  • labeling(v.0.4.2)
  • pkgload(v.1.3.0)
  • nlme(v.3.1-147)
  • statsExpressions(v.1.1.0)
  • nnet(v.7.3-14)
  • rlang(v.1.1.0)
  • lifecycle(v.1.0.3)
  • miniUI(v.0.1.1.1)
  • MatrixModels(v.0.5-0)
  • sandwich(v.3.0-2)
  • extrafontdb(v.1.0)
  • dichromat(v.2.0-0.1)
  • cellranger(v.1.1.0)
  • tcltk(v.4.2.1)
  • rprojroot(v.2.0.3)
  • datawizard(v.0.4.1)
  • Matrix(v.1.2-18)
  • mc2d(v.0.1-21)
  • zoo(v.1.8-10)
  • base64enc(v.0.1-3)
  • processx(v.3.6.1)
  • png(v.0.1-7)
  • viridisLite(v.0.4.1)
  • PMCMRplus(v.1.9.4)
  • parameters(v.0.18.1)
  • blob(v.1.2.3)
  • multcompView(v.0.1-8)
  • readr(v.2.1.2)
  • jpeg(v.0.1-9)
  • ggsignif(v.0.6.3)
  • scales(v.1.2.1)
  • memoise(v.2.0.1)
  • magrittr(v.2.0.3)
  • plyr(v.1.8.7)
  • compiler(v.4.2.1)
  • snakecase(v.0.11.0)
  • cli(v.3.6.1)
  • pbapply(v.1.5-0)
  • ps(v.1.7.1)
  • htmlTable(v.2.4.0)
  • MASS(v.7.3-51.6)
  • tidyselect(v.1.2.0)
  • mixmeta(v.1.2.0)
  • highr(v.0.9)
  • yaml(v.2.3.5)
  • latticeExtra(v.0.6-29)
  • sass(v.0.4.2)
  • tools(v.4.2.1)
  • parallel(v.4.2.1)
  • uuid(v.1.1-0)
  • rstudioapi(v.0.13)
  • foreign(v.0.8-79)
  • ipmisc(v.6.0.2)
  • pairwiseComparisons(v.3.1.6)
  • farver(v.2.1.1)
  • digest(v.0.6.29)
  • shiny(v.1.7.2)
  • BWStest(v.0.2.2)
  • Rcpp(v.1.0.10)
  • BayesFactor(v.0.9.12-4.4)
  • performance(v.0.9.1)
  • later(v.1.3.0)
  • httr(v.1.4.5)
  • gdtools(v.0.2.4)
  • effectsize(v.0.7.0)
  • colorspace(v.2.0-3)
  • fs(v.1.5.2)
  • rvest(v.1.0.2)
  • splines(v.4.2.1)
  • rematch2(v.2.1.2)
  • mapproj(v.1.2.8)
  • renv(v.0.14.0)
  • systemfonts(v.1.0.4)
  • xtable(v.1.8-4)
  • gmp(v.0.6-5)
  • jsonlite(v.1.8.4)
  • zeallot(v.0.1.0)
  • modeltools(v.0.2-23)
  • chromote(v.0.1.0)
  • R6(v.2.5.1)
  • pillar(v.1.9.0)
  • htmltools(v.0.5.3)
  • mime(v.0.12)
  • glue(v.1.6.2)
  • fastmap(v.1.1.0)
  • codetools(v.0.2-19)
  • maps(v.3.4.0)
  • pkgbuild(v.1.3.1)
  • utf8(v.1.2.3)
  • bslib(v.0.4.0)
  • curl(v.5.0.0)
  • officer(v.0.4.3)
  • zip(v.2.2.0)
  • Rttf2pt1(v.1.3.10)
  • munsell(v.0.5.0)
  • gtable(v.0.3.1)
  • bayestestR(v.0.12.1)
  • extrafont(v.0.18)


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