Quantitative Resilience Research across Cultures and Contexts Fons J. R. van de Vijver

Outline 4. Acculturation 5. Test adaptations 1. General introduction Tertium comparationis Approaches: Absolutism/relativism/universalism Identity of meaning 2. Common problems of cross-cultural studies (and their solutions) 3. Establishing similarity of meaning: 3a. Bias and equivalence: Taxonomies 3b. Examples 4. Acculturation Concepts and Models / Assessment 5. Test adaptations Concepts / Example

General Introduction Conceptual core of cross-cultural studies Aim is to compare constructs or scores Is resilience the same across the globe? Is Country A more/less resilient than Country B? Comparison always implies some shared quality (tertium comparationis): If a comparison visualizes an action, state, quality, object, or a person by means of a parallel which is drawn to a different entity, the two things which are being compared do not necessarily have to be identical. However, they must possess at least one quality in common. This common quality has traditionally been referred to as tertium comparationis (Source: http://en.wikipedia.org/wiki/Tertium_comparationis).

Views on the Relation between Resilience and Culture 1. Absolutism (“etic”) Resilience refers to a universal set of characteristics that individuals use to cope with and thrive despite adversity 2. Relativism Resilience refers to a concept (dealing with coping and thriving) that is universally applicable; however, its manifestations may differ across cultures Example: Zimmerman & Brenner (2010, referring to Ungar, 2006) The conceptual foundation of resiliency theory can be applicable across cultures; the extent to which resources and assets are applied by youth in their experiences of adversity, however, may not be consistent across all contexts. 3. Relativism (“emic”) Resilience refers to basic concept of coping and thriving; however, link between resilience and cultural context is so close that cross-cultural comparisons of resilience are futile and superficial

Choice between models is often made on an ideological basis However, more productive to see absolutism and relativism as extremes along a continuum Empirical studies possible of adequacy of these viewpoints Cross-cultural evidence is vital for determining which viewpoint holds for a particular measure/construct

Part 2 What are common problems in comparative studies? Central problem: Identity of meaning 6

Common methodological problems of cross-cultural research and their solutions

Problem 1 Cross cultural differences in scores cannot be interpreted due to rival hypotheses Particularly salient in two-culture studies that do not consider contextual factors Solution: Anticipate on rival hypotheses by including more cultures or measuring contextual factors

Problem 2 Cross-cultural similarities and differences are visually (and not statistically) tested A common example is the absence of a test of similarities of internal consistency coefficients Solution Explicit tests of cross-cultural similarities and differences; e.g., simple test of similarity of independent reliabilities available

Test of Independent Reliabilities

Problem 3 Samples show confounding differences Solution: Particularly salient in convenience sampling Solution: Adaptation of study design and assessment of confounding differences

Problem 4 Means of different cultural groups are compared without assessing the equivalence Particularly salient when studying new instruments or working with cultures in which instrument has not been used Solution: Assessment of structural and metric equivalence; assessment of structural equivalence/differential item functioning should be a routine part of analysis, similar to routine assessment of internal consistency

Problem 5 Cultural characteristics are attributed to all individuals of that culture (ecological fallacy) Particularly common in studies of individualism—collectivism Solution: Awareness of distinction between individual-and culture-level characteristics Assessment of relevant characteristics, such as individualism—collectivism, at individual level

Problem 6 No check on quality of translation/ adaptation Solution: Check is often not reported or procedure is poorly specified (e.g., translation back translation has been used, but results of procedure are not reported) Solution: Awareness that translation back translation is not always the best possible method; other approaches, such as committee approach, may be more suitable More detail in reports about translation/adaptation procedure

Problem 7 Lack of rationale for selecting cultures Solution: Convenience sampling of cultures is by far the most common procedure in cross-cultural psychology; most common comparison is between Japan and the US Solution: Explain why the culture was chosen

Problem 8 There is a verification bias in studies of common paradigms Particularly salient in studies of individualism –collectivism Solution: More critical appreciation of the boundaries of the construct, more focus on falsification

Problem 9 There is a focus on the statistical significance of cross-cultural differences In the first and two related problems: Implicit goal of cross-cultural psychology is not the establishment of cross-cultural differences Focus on significance detracts attention from effect sizes Solution: Balanced treatment of similarities and differences; differences easier to interpret against a backdrop of similarities More effect sizes should be reported, such as Cohen’s d and (partial) eta squares.

Problem 10 Results are generalized to large populations, often complete populations of countries, although no probability sampling has been employed to recruit participants Particularly salient in convenience sampling of participants (often student samples) Solution More attention in reports for sampling frame and for consequences on external validity

Part 3a Bias and equivalence: Definitions of concepts A framework

(a) Bias and Equivalence Does the test measure the same attributes for all cultural groups? Can scores be compared across ethnic groups?

Bias: Taxonomy What is internal bias? General: dissimilarity of psychological meaning across cultural groups Practical: when cross-cultural differences do not involve target construct measured by the test Theoretical: a cross-cultural comparison is biased when observed cross-cultural differences (in structure or level) cannot be fully interpreted in terms of the domain of interest

Taxonomy of Bias

Construct Bias Partial nonoverlap of behaviors defining construct González Castro & Murray (2010): Criteria for resilience are based on studies with U.S. youth and adults, and one important cross-cultural issue involves how these criteria, as Westernized aspects of resilience, may or may not relate to resilience that is manifest in underdeveloped and/or non-Western countries. Examples: Ho (1996): filial piety: Chinese concept is broader than Western concept Socioeconomic status: upbringing, education, income and profession Gender bias in a study of adolescents in the Netherlands about body perception: sexual attractiveness was related to physical attractiveness for women and to physical strength for men 8

Definition of happiness in individualistic and collectivistic countries? Example: Uchida, Norasakkunkit and Kitayama (2004):

Types and Sources of Method Bias Method bias tends to have a global influence on cross-cultural score differences (e.g., increment due to social desirability)

Item Bias (also known as differential item functioning, DIF) Informal description Differences in psychological meaning of stimuli, due to anomalies at item level More formal definition: An item of a scale (e.g., measuring anxiety) is said to be biased if persons with the same trait anxiety, but coming from different cultures, are not equally likely to endorse the item.

Example of Biased Item

Types of (un)biased items

Analysis of Variance and Item Bias Item behavior examined per item We do not test for cultural differences, but we test whether scores are identical for persons from different groups with an equal proficiency Note: regression approach quite similar (illustrated later)

Taxonomy of Equivalence Refers to level of comparability Is related to bias: Highest level of equivalence obtained for bias-free measurement Examples: Underrepresentation of own region will lead to an underestimation of geographical knowledge Knowledge of the word “bacon” may well point to level of acculturation (adaptation) Will strongly reflect legal system or subgroup norms Influence of social desirability 4

Types of Equivalence Three types: 1. “Structural” or “functional equivalence” 2. “Metric equivalence” or “measurement unit equivalence” 3. “Scalar equivalence” or “full score equivalence” 9

(a) “Structural” or “Functional Equivalence” Measurement of the same traits Various statistical tools available, e.g., exploratory factor analysis (with target rotation) confirmatory factor analysis nomological networks (particularly relevant when items/questions are not identical across cultures) Qualitative equivalence can be firmly established Interest is here NOT in a comparison of scores across cultures, but in the question of underlying structure: Is it reasonable to assume that the same construct is measured in each cultural group? Differences in scores of various cultural groups may be due factors like stimulus familiarity and social desirability (assuming that these affect all items in more or less the same way) 10

(b) “Metric Equivalence”, “Measurement Unit Equivalence” Difference in offset of scales of cultural groups, equal measurement units Individual differences have a different meaning within and across cultures: no problems with offset in intra-cultural comparison, offset has to be added in cross-cultural comparison Statistical tool: structural equation modeling (confirmatory factor analysis) Example: Measurement of temperature in degrees of Celsius and Kelvin (consistent difference of 273 degrees) In psychology such an offset can be created by factors like social desirability or stimulus familiarity 13

(c) “Scalar Equivalence” or “Full Score Equivalence” Complete comparability of scores, both within and across cultures; seamless transfer of scores across cultures Frequently taken as the aim of cross-cultural research For physical variables full score equivalence is not hard to achieve (e.g., measurement of height and blood pressure) To demonstrate that a psychological test shows full score equivalence is usually quite complicated 14

Comparability and Equivalence Levels Structural Underlying construct Metric Same plus score metric Scalar Same plus origin of scale

Part 3b Establishing similarity of meaning How to determine equivalence? How to determine item bias? 36

Many statistical procedures available for testing structural equivalence Common approach: Apply dimensionality-reduction technique Compare underlying dimensions across cultures Similarity of underlying dimensions is criterion for similarity of meaning

Testing Structural Equivalence: Exploratory Factor Analysis 38

Two procedures explained 1. Pairwise comparisons Compare all cultures in a pairwise manner 2. “One to all” comparison Compare all cultures to a global, pooled solution

Characteristics of pairwise comparisons Strong point: much detail, all pairs compared Weak point: computationally cumbersome, difficult to integrate Characteristics of pooled comparisons Strong point: maintains overview, integration Weak point: can conceal subgroups of countries

Example Pairwise Data set: WISC-III administered in Canada and Netherlands/Flanders 41

Sample 42

12 Subtests Picture Completion Information Coding Similarities Picture Arrangement Arithmetic Block Design Vocabulary Object Assembly Comprehension Symbol Search Digit Span 43

Analysis Steps Determine number of factors in combined sample Carry out factor analyses per group Compare factors across groups Note: analysis of scaled scores 44

1. Determining Number of Factors 45

1. Determining Number of Factors Scree plot suggests the extraction of a single factor Literature: Debate about 3 or 4 factors Hierarchical model of correlated factors Here: 4 factors 46

2. Factor Analyses per group: Oblimin-Rotated Solution 47

2. Factor Analyses per group: Oblimin-Rotated Solution 48

3. Compare Factors across Groups Rotate one solution to the other Target rotations to deal with rotational freedom in factor analysis Evaluation by means of Tucker’s phi (factor congruence coefficient): similarity of factors up to multiplying (positive) constant (correct for differences in eigenvalues across cultures) 49

3. Compare Factors across Groups Formula (x and y are loadings after target rotation of one to the other): 50

3. Compare Factors across Groups 51

3. Compare Factors across Groups Values above .90 are usually considered to be adequate and values above .95 to be excellent Such high values point to similarity of factors  structural equivalence 52

3. Compare Factors across Groups Dedicated software needed to compute Tucker’s phi SPSS routine available 53

Belg./Neth. rotated 54

PROPORTIONALITY COEFFICIENT per Factor: .99 .98 .97 .91 .99 .98 .97 .91 55

Conclusion Strong evidence for similarity of first two factors Less convincing for third and fourth factor 56 56

Example “One to All” Steps in analysis: 1. Exploratory factor analysis on the total data set; Two procedures (note: correct for mean differences between groups): “quick and dirty”: standardize scores per cultural groups and factor analyze the standardized scores more adequate solution: compute the weighted average of the covariance matrices of the cultural groups (weight by sample size) this factor analysis provides the “pooled solution”

“One-to-all” procedure 2. Carry out a factor analysis in each cultural group 3. Compute agreement of the pooled solution and each of the country solutions Source: Van de Vijver, F.J.R. & Poortinga, Y.H. (2002). Structural Equivalence in Multilevel Research. Journal of Cross-Cultural Psychology.

Example 1990-1991 World Values Survey (Inglehart, 1993, 1997) 47,871 respondents from the following 39 “regions” (number of respondents in parentheses): Austria (1355), Belarus (912), Belgium (2318), Brazil (1672), Bulgaria (877), Canada (1545), Chile (1368), China (960), (the former) Czechoslovakia (1384), Denmark (892), (the former) East Germany (1226), Estonia (864), Finland (416), France (902), Hungary (886), Iceland (659), India (2150), Ireland (976), Italy (1810), Japan (655), Latvia (720), Lithuania (847), Mexico (1193), Moscow (894), Netherlands (935), Nigeria (954), Northern Ireland (283), Norway (1111), Poland (850), Portugal (976), Russia (1642), South Africa (2480), South Korea (1210), Spain (3408), Sweden (901), Turkey (886), United Kingdom (1356), United States (1688), and (the former) West Germany (1710).

Instrument

Pooled solution (Sign of loadings in line with expectation)

Stem-and-Leaf Display of Agreement Pooled Loadings and Factor Loadings per Country

Correlations of GNP and the Loadings per Region on the Postmaterialism Scale Conclusion: Postmaterialism concept more salient in more affluent countries

Metric Equivalence at Scale Level: Structural Equation Modeling 64

Difference with Exploratory Factor Analyses Starts from covariance matrices Use metric information More parameters tested for cross-cultural similarity; examples Factor loadings Factor correlations/covariances Error component of latent variables Error component of observed variables Enables the testing of a hierarchy of models

Example of AMOS Model tested: one factor of verbal comprehension factor in two countries (Belgium/Netherlands and Canada) Models tested: Identical factor loadings across countries Free factor loadings Idem with a correlated error For diagram and output: see AMOS files 66

Basic Model e1 INFORMAT e2 SIMILARI intelligence e6 e3 ARITHMET e4 VOCABULA 1 e5 COMPREHE 67

Use of multiple group option 68

Measurement weights: regression weights in the measurement part of the model. In the case of a factor analysis model, these are the "factor loadings". Structural residuals: variances and covariances of residual (error) variables in the structural part of the model. Measurement residuals: variances and covariances of residual (error) variables in the measurement part of the model. 69

AMOS model e1 INFORMAT e2 SIMILARI intelligence e6 e3 ARITHMET e4 b 1 1 intelligence e6 e3 ARITHMET c d 1 e4 VOCABULA 1 e5 COMPREHE Measurement weights 70

AMOS model e1 INFORMAT e2 SIMILARI intelligence e3 ARITHMET e4 b 1 1 intelligence e6 e3 ARITHMET c d 1 e4 VOCABULA 1 e5 COMPREHE Structural residuals 71

AMOS model INFORMAT SIMILARI intelligence e6 ARITHMET VOCABULA 1 e1 INFORMAT 1 1 a e2 SIMILARI b 1 1 intelligence e6 e3 ARITHMET c d 1 e4 VOCABULA 1 e5 COMPREHE Measurement residuals 72

BelgNeth - Unconstrained Estimate S.E. C.R. P Label COMPREHE <--- intelligence .952 .042 22.661 *** a1_1 VOCABULA 1.144 .043 26.736 a2_1 ARITHMET .801 .036 22.415 a3_1 SIMILARI 1.031 24.720 a4_1 INFORMAT 1.000 Regression Weights: (Canada - Unconstrained) Canada Estimate S.E. C.R. P Label COMPREHE <--- intelligence .874 .040 21.770 *** a1_2 VOCABULA 1.158 .041 28.323 a2_2 ARITHMET .780 .038 20.796 a3_2 SIMILARI 1.056 .039 26.886 a4_2 INFORMAT 1.000 73

Measurement residuals 11 66.732 19 3.512 Saturated model 30 CMIN Model NPAR CMIN DF P CMIN/DF Unconstrained 22 47.982 8 .000 5.998 Measurement weights 18 51.793 12 4.316 Structural residuals 17 53.049 13 4.081 Measurement residuals 11 66.732 19 3.512 Saturated model 30 Independence model 10 5084.104 20 254.205 74

Measurement residuals .227 .988 .982 .626 Saturated model .000 1.000 RMR, GFI Model RMR GFI AGFI PGFI Unconstrained .157 .992 .970 .265 Measurement weights .185 .991 .978 .397 Structural residuals .241 .979 .429 Measurement residuals .227 .988 .982 .626 Saturated model .000 1.000 Independence model 4.034 .450 .175 .300 75

Measurement residuals .033 .025 .042 1.000 Independence model .330 RMSEA Model RMSEA LO 90 HI 90 PCLOSE Unconstrained .046 .034 .059 .658 Measurement weights .038 .028 .049 .969 Structural residuals .036 .027 .047 .985 Measurement residuals .033 .025 .042 1.000 Independence model .330 .322 .338 .000 76

77

Metric Equivalence at Item Level: Item Bias Analysis/ Differential Item Functioning (DIF) 78

Hundreds of statistical procedures available Assumption: Equal observed scores on global instrument (scale) in different cultures have the same meaning Almost all techniques start from unidimensional scales Procedures test whether, given equal total scores, patterns of observed scores are the same across cultures Often applied procedures ANOVA (example follows) Item Response Theory (in education) Mantel-Haenszel (equivalent to testing applicability of Rasch model)

How to Determine Item Bias? Analysis of variance INPUT: a data matrix with interval-level dependent variables (e.g., Likert-scale), one variable indicating culture. 80

Step 1: Compute Total Score Compute total test score (or mean item score) (so, a unifactorial scale is assumed). COMPUTE sumscore = i_acad_1 + i_cult_1 + i_groo_1 + i_infl_1 + i_inte_1 + i_like_1 + i_look_1 . EXECUTE . 81

Step 2: Determine Cutoffs (here three groups; percentiles 33 and 67). EXAMINE VARIABLES=sumscore /PLOT BOXPLOT STEMLEAF /COMPARE GROUP /PERCENTILES(33, 67) HAVERAGE /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL. OR FREQUENCIES VARIABLES=sumscore /NTILES= 3 /ORDER= ANALYSIS . 82

Step 3: Compute Level RECODE sumscore (Lowest thru 48=1) (49 thru 57=2) (58 thru Highest=3) (ELSE=SYSMIS) INTO level . VARIABLE LABELS level 'Score level'. EXECUTE . 83

Step 4: Carry out ANOVAs Significant main effect of level: irrelevant UNIANOVA i_acad_1 i_cult_1 i_groo_1 i_infl_1 i_inte_1 i_like_1 i_look_1 BY group level /METHOD = SSTYPE(3) /INTERCEPT = INCLUDE /PRINT = DESCRIPTIVE ETASQ /CRITERIA = ALPHA(.05) /DESIGN = group level group*level . Significant main effect of level: irrelevant Significant main effect of culture: uniform bias Significant interaction between culture and level: nonuniform bias NOTE: in large samples effect sizes can be used (eta squared > .06: Cohen’s medium effect size) 84

Regression DESCRIPTIVES VARIABLES=sumscore cult /STATISTICS=MEAN STDDEV MIN MAX.

compute predictor values for these new variables * compute predictor values for these new variables. compute dev_mean=sumscore-52.6091. compute dev_cult=cult-1.4473. EXECUTE . compute interaction = dev_mean*dev_cult.

REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT i_acad_1 /METHOD=ENTER sumscore /METHOD=ENTER cult /METHOD=ENTER interaction.

Part 4. Acculturation Definition: Acculturation refers to changes that take place as a result of continuous first-hand contact between individuals of different cultural origins (Redfield, Linton, & Herskovits, 1936). Psychological acculturation refers to psychological aspects of process

Acculturation research traditions:  Stress and coping  Social learning  Social cognition (more recent)

Framework of Acculturation: Acculturation Variables Acculturation Conditions Acculturation Orientations Acculturation Outcomes Characteristics of the receiving society (e.g., discrimination, opportunity structures) Cultural adoption Psychological well-being (psychological distress, mood states, feelings of acceptance, and satisfaction) Characteristics of the society of origin (objective, perceived) Sociocultural competence in ethnic culture (interaction with conationals, maintenance of culturally appropriate skills and behaviors) Cultural maintenance Characteristics of the immigrant group (objective, perceived) Personal characteristics Sociocultural competence in mainstream culture (interaction with hosts, acquisition of culturally appropriate skills and behaviors)

Features Compare S-O-R model Mediation model with feedback loops Feedback almost never studied Causality usually inferred (so, some arbitrariness) Implicit scheme distal—proximal—output Term adaptation used in literature to refer to adjustment/output Problem: adaptation can refer to both product and process

Resilience-Related Pathways for Immigrants (González Castro & Murray, 2010)

Studies of Acculturation Conditions Personality often studied MPQ, Big Five Usually: extraversion +, neuroticism – Intelligence not studied Multiculturalism policies presumably unrelated to acculturation outcomes in Western societies ESS (Schalk-Soekar et al., 2007) ICSEY (Berry et al., 2006)

2 examples Perceived acculturation context Perceived cultural distance

Structure of Perceived Environment Mainstream context:

Minority context:

Role of (perceived) cultural distance

Dimensionality of Cultural Distance Psychological measures of distance (perceived cultural distance) load on a single factor Note: models of cross-cultural distance models tend to be multidimensional (e.g., Hofstede)

Acculturation Orientations Notes on terminology: 1. Various terms used, e.g., Strategies, styles, orientations 2. Adaptation usually reserved for output/adjustment; here: adoption, adopting in original formulation: does the immigrant want to establish relationships with new culture? Problem: Narrow conceptualization

Cultural adoption Cultural maintenance maintaining characteristics of own (heritage) culture Cultural adoption adopting characteristics of the culture of the society of settlement

Acculturation Models  Unidimensional model  Bidimensional model Cultural Cultural maintenance adoption Cultural maintenance Cultural adoption

Berry’s Bidimensional Model Yes Separation Integration Cultural maintenance? No Marginalization Assimilation No Yes Cultural adoption?

Features Correlations of dimensions often vary Conceptually independent Empirically often negatively related Dimensions or orientations more important? Methodologically: dimensions often easier to deal with Conceptually: orientations prevail Note that integration refers to biculturalism in psychology and to sociocultural outcomes in sociology (a well integrated immigrant is a person who speaks the mainstream language, has a paid job, etc.)

Fusion Model New culture Cultural maintenance Cultural adoption

Domain Specificity Conceptually domains independent Empirically not always the case Will depend on a host of factors, such as cultural distance, perceived pressure to assimilate, … Often slightly negative correlations Example: we found a clear negative corelation in the evaluations of Dutch and Turkish culture in a group of Turkish-Dutch

Assessment of Acculturation: Recurrent Problems Acculturation variables (conditions, orientations, and outcomes) are mixed Reliance on ‘Proxy’ measures of acculturation, such as length of stay (poor validity) Reliance on single-index measures (do not fully account for construct)

Assessment of Acculturation: Recurrent Problems (cont’d) Measure of only adoption dimension, not of maintenance dimension Acculturation aspects (e.g., cognition, values, attitudes) are often combined. Sound and meaningful? No psychometric properties reported Often emphasis on actual behavior and language proficiency Measures often assess sociocultural outcomes that are used to predict other outcomes (e.g., school performance)

Outcomes Focus on two kinds of outcomes Psychological adjustment (stress & coping) Sociocultural adjustment (social learning) Almost no studies of cultural maintenance This lack of balance absent in sociolinguistics where both acquisition of mainstream and loss of ethnic languages is studied This lack of balance is also absent in study of acculturation orientations

Measurement Methods  Unidimensional model: (1) One-statement method (more - less)  Bidimensional model: (2) Two-statement method (maintenance; adoption) (3) Four-statement method (acculturation strategies)

(1) One-Statement Method  Example item (1 statement for 1 domain)  only Turkish friends.  more Turkish than Dutch friends.  I find it important to have  as many Turkish as Dutch friends.  more Dutch than Turkish friends.  only Dutch friends.  no Dutch and no Turkish friends.  Advantages  Short(est) questionnaire Problem  One dimension? Heritage Mainstream

(1) One-Statement Method  Research findings  Domain specificity (public, private components) public Dutch private Turkish  Recommendation  This method is often quite useful in practice, despite conceptual problems  Take domains into consideration

(2) Two-Statement Method  Example (domain friends)  I think it is important to have Dutch friends. 1 2 3 4 5 6 7  I think it is important to have Turkish friends. 1 2 3 4 5 6 7  Advantages  The two dimensions are measured independently  Items are not complex  Questionnaire is still short  Disadvantages/questions  Are the two dimensions really independent?  How to define the four acculturation orientations?

How to Define the Four Acculturation Orientations? Sample-dependent coding: Mean or (more common) median split Advantage: optimal spread of participants across orientations Disadvantage: validity can be problematic in groups with a shared preference (often the case for integration)

How to Define the Four Acculturation Orientations? (cont’d) Response scale-dependent coding Midpoint split (average scores above or below midpoint of scale) Advantage: face validity Disadvantage: what to do when scale has even number of anchors? Solutions such as random split or allocating these to a single group have an unavoidable arbitrariness

(2) Two-Statement Method Results Possible method factor, e.g., all maintenance items together Domain dependence:  public domain (Tu, Du)  private Dutch domain  private Turkish domain Domain dependence does not always show up as separate factors (usually based on differences in mean scores)

Potential problem: Two scores are sometimes converted to four orientations (e.g., distance method), which introduces dependencies in the data Recommendation  This method can be used  Take domains into consideration

Acculturation Strategies 7 6 5 4 3 2 1 Private Public Cultural maintenance (Tu) 1 2 3 4 5 6 7 Cultural adoption (Du)

Summary of Results Results of the ‘one-statement’ and the ‘two-statement’ measurement methods: domain specificity Public Dutch Private Turkish 7 6 5 4 3 2 1 Private Public Cultural maintenance (Turkish) 1 2 3 4 5 6 7 Cultural adoption (Dutch)

(3) Four-Statement Method  Example item (4 items for 1 domain)  (Int) I find it important to have Dutch friends and 1 2 3 4 5 I find it also important to have Turkish friends.  (Sep) I find it not important to have Dutch friends 1 2 3 4 5 but I find it important to have Turkish friends.  Advantage  The four strategies are measured independently  Disadvantages (questions)  Complex items (see Marginalization)  Questionnaire is long (per domain 4 questions)  Factors and (independent) dimensions?

(3) Four-Statement Method  Research findings  Bipolar unidimensional structure (-) Integration (+) A S M  80-85% of our immigrant Dutch samples prefer integration (one score)  Advantages  Method is broad  Measure integration with more details

Summary of Results Measurement Results methods  Four-statement Insufficient discrimination: integration vs not-integration  One-statement Discrimination between public and private domains  Two-statement More detailed information within domains Two-statement method often works best.

Questions to consider when choosing/designing an instrument 1. The clear formulation of research goals and choice of acculturation variables. What is the role of acculturation in the study? Antecedent, mediating/moderating, or outcome variable

2. Which acculturation aspects are dealt with? knowledge, values, attitudes, or behavior

3. The choice of research methodology (how to study?) “Soft” or “hard” measures Self-reports, observations, …

4. The choice of a measurement method (how to assess acculturation?) Orientations: one-, two-, and four-statement method Perceived or actual environmental conditions Multilevel issues may be involved when both individual and contextual variables are considered

5. The choice of life domains and situations to be dealt with in the items in which domains and situation to assess?

6. Choice of item wording. Questionnaires often in second language Use simple language

An Empirical Study  Methods (dimensions) of acculturation  (1) One-statement method  (2) Two-statement method  (3) Four-statement method  Domain(s) of acculturation  Private domains (celebrations, child-rearing)  Public domains (language, education, living)

Participants  293 Turkish-Dutch adolescents  Gender: 144 female and 149 male  Generation: 15 first and 278 second generations  Age: 11 - 19 years, M = 14.67 (SD = 1.69)  Education: Secondary School Instrument and procedure  (1) 15 items on 15 domains (7 private and 8 public)  (2) 30 items on 15 domains (7 private and 8 public)  (3) 36 items on 9 domains (5 private and 4 public)

A C U L T R I O N M E A S U R N T

Summary of Results  Measurement methods of acculturation  One- and two-statement methods: no significant influences of measurement on outcome  Four-statement method: the largest influence on outcome  Domain specificity  Distinct but interrelated positive relationship between private and public domains