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

2. 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

3. 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:

4. 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

5. 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

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

7. Common methodological problems of cross-cultural research and their solutions

8. 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

9. 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

10. Test of Independent Reliabilities

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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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.

18. 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

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

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

21. 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

22. Taxonomy of Bias

23. 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

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

25. 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)

26. 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.

27. Example of Biased Item

28. Types of (un)biased items

29. 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)

30. 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

31. 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

32. (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

33. (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

34. (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

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

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

37. 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

38. Testing Structural Equivalence:
Exploratory Factor Analysis 38

39. 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

40. 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

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

42. Sample 42

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

44. 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

45. 1. Determining Number of Factors45

46. 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

47. 2. Factor Analyses per group: Oblimin-Rotated Solution47

48. 2. Factor Analyses per group: Oblimin-Rotated Solution48

49. 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

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

51. 3. Compare Factors across Groups51

52. 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

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

54. Belg./Neth. rotated 54

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

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

57. 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”

58. “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.

59. 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).

60. Instrument

61. Pooled solution
(Sign of loadings in line with expectation)

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

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

64. Metric Equivalence at Scale Level:
Structural Equation Modeling 64

65. 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

66. 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

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

68. Use of multiple group option68

69. 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

70. AMOS model e1 INFORMAT e2 SIMILARI intelligence e6 e3 ARITHMET e4b 1 1
intelligence e6 e3
ARITHMET c d 1 e4
VOCABULA 1 e5
COMPREHE
Measurement weights 70

71. AMOS model e1 INFORMAT e2 SIMILARI intelligence e3 ARITHMET e4b 1 1
intelligence e6 e3
ARITHMET c d 1 e4
VOCABULA 1 e5
COMPREHE
Structural residuals 71

72. AMOS model INFORMAT SIMILARI intelligence e6 ARITHMET VOCABULA1 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

73. 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

74. 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 20 74

75. 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

76. 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. 77

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

79. 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)

80. 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

81. 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

82. 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

83. 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

84. 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

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

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

87. 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.

88. 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

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

90. 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)

91. 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

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

93. 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)

95. 2 examples
Perceived acculturation context
Perceived cultural distance

96. Structure of Perceived Environment
Mainstream context:

97. Minority context:

98. Role of (perceived) cultural distance

101. 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)

102. 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

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

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

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

106. 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.)

107. Fusion Model
New culture
Cultural
maintenance
Cultural adoption

108. 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

110. 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)

111. 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)

112. 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

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

114. (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

115. (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

116. (2) Two-Statement Method
 Example (domain friends)
 I think it is important to have Dutch friends
 I think it is important to have Turkish friends
 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?

117. 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)

118. 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

119. (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)

120. 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

121. Acculturation Strategies7 6 5 4 3 2 1
Private
Public
Cultural
maintenance
(Tu)
Cultural adoption (Du)

122. 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)
Cultural adoption (Dutch)

123. (3) Four-Statement Method
 Example item (4 items for 1 domain)
 (Int) I find it important to have Dutch friends and I find it also important to have Turkish friends.
 (Sep) I find it not important to have Dutch friends 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?

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

125. 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.

126. 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

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

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

129. 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

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

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

132. 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)

133. Participants  293 Turkish-Dutch adolescents
 Gender: 144 female and 149 male
 Generation: 15 first and 278 second generations
 Age: years, M = (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)

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

135. 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