Quantitative Resilience Research across Cultures and Contexts

1
Quantitative Resilience Research across Cultures
and Contexts

• Fons J. R. van de Vijver

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Outline

• 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 http//en.wikipedia.org/wik
  i/Tertium_comparationis).

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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

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• 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

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Part 2

• What are common problems in comparative studies?
• Central problem
• Identity of meaning

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• Common methodological problems of cross-cultural
  research and their solutions

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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

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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

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Test of Independent Reliabilities
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Problem 3

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

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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

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Problem 5

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

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Problem 6

• No check on quality of translation/ adaptation
• 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/adaptatio
  n procedure

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Problem 7

• Lack of rationale for selecting cultures
• 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

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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

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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
  Cohens d and (partial) eta squares.

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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

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Part 3a

• Bias and equivalence
• Definitions of concepts
• A framework

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(a) Bias and Equivalence

• Does the test measure the same attributes for all
  cultural groups?
• Can scores be compared across ethnic groups?

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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

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Taxonomy of Bias

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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.

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

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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)
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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.

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Example of Biased Item
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Types of (un)biased items
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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)

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Taxonomy of Equivalence

• Refers to level of comparability
• Is related to bias
• Highest level of equivalence obtained for
  bias-free measurement

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Types of Equivalence

• Three types
• 1. Structural or functional equivalence
• 2. Metric equivalence or measurement unit
  equivalence
• 3. Scalar equivalence or full score
  equivalence

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(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

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(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)

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(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

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Comparability and Equivalence Levels
Equivalence Comparability
Structural Underlying construct
Metric Same plus score metric
Scalar Same plus origin of scale
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Part 3b

• Establishing similarity of meaning
• How to determine equivalence?
• How to determine item bias?

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• 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

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Testing Structural Equivalence

• Exploratory Factor Analysis

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• 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

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• 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

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Example Pairwise

• Data set WISC-III administered in Canada and
  Netherlands/Flanders

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Sample
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12 Subtests

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

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Analysis Steps

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

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1. Determining Number of Factors
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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

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2. Factor Analyses per group Oblimin-Rotated
Solution
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2. Factor Analyses per group Oblimin-Rotated
Solution
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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 Tuckers phi (factor
  congruence coefficient)
• similarity of factors up to multiplying
  (positive) constant (correct for differences in
  eigenvalues across cultures)

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3. Compare Factors across Groups

• Formula (x and y are loadings after target
  rotation of one to the other)

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3. Compare Factors across Groups
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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

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3. Compare Factors across Groups

• Dedicated software needed to compute Tuckers phi
• SPSS routine available

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Belg./Neth. rotated
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PROPORTIONALITY COEFFICIENT per Factor .99
.98 .97 .91
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Conclusion

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

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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

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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.
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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).

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Instrument
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Pooled solution
(Sign of loadings in line with expectation)
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Stem-and-Leaf Display of Agreement Pooled
Loadings and Factor Loadings per Country
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Correlations of GNP and the Loadings per Region
on the Postmaterialism Scale
Conclusion Postmaterialism concept more salient
in more affluent countries
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Metric Equivalence at Scale Level

• Structural Equation Modeling

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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

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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

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Basic Model
1
INFORMAT
e1
1
1
SIMILARI
e2
1
intelligence
e6
1
ARITHMET
e3
1
VOCABULA
e4
1
COMPREHE
e5
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• Use of multiple group option

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• 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.

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AMOS model
1
INFORMAT
e1
1
1
a
SIMILARI
e2
b
1
intelligence
e6
1
c
ARITHMET
e3
d
1
VOCABULA
e4
1
COMPREHE
e5
Measurement weights
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AMOS model
1
INFORMAT
e1
1
1
a
SIMILARI
e2
b
1
intelligence
e6
1
c
ARITHMET
e3
d
1
VOCABULA
e4
1
COMPREHE
e5
Structural residuals
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AMOS model
1
INFORMAT
e1
1
1
a
SIMILARI
e2
b
1
intelligence
e6
1
c
ARITHMET
e3
d
1
VOCABULA
e4
1
COMPREHE
e5
Measurement residuals
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BelgNeth - Unconstrained BelgNeth - Unconstrained BelgNeth - Unconstrained Estimate S.E. C.R. P Label
COMPREHE lt--- intelligence .952 .042 22.661 a1_1
VOCABULA lt--- intelligence 1.144 .043 26.736 a2_1
ARITHMET lt--- intelligence .801 .036 22.415 a3_1
SIMILARI lt--- intelligence 1.031 .042 24.720 a4_1
INFORMAT lt--- intelligence 1.000
Regression Weights (Canada - Unconstrained)
Canada Estimate S.E. C.R. P Label
COMPREHE lt--- intelligence .874 .040 21.770 a1_2
VOCABULA lt--- intelligence 1.158 .041 28.323 a2_2
ARITHMET lt--- intelligence .780 .038 20.796 a3_2
SIMILARI lt--- intelligence 1.056 .039 26.886 a4_2
INFORMAT lt--- intelligence 1.000
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CMIN
Model NPAR CMIN DF P CMIN/DF
Unconstrained 22 47.982 8 .000 5.998
Measurement weights 18 51.793 12 .000 4.316
Structural residuals 17 53.049 13 .000 4.081
Measurement residuals 11 66.732 19 .000 3.512
Saturated model 30 .000 0
Independence model 10 5084.104 20 .000 254.205
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RMR, GFI
Model RMR GFI AGFI PGFI
Unconstrained .157 .992 .970 .265
Measurement weights .185 .991 .978 .397
Structural residuals .241 .991 .979 .429
Measurement residuals .227 .988 .982 .626
Saturated model .000 1.000
Independence model 4.034 .450 .175 .300
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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
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Metric Equivalence at Item Level

• Item Bias Analysis/
• Differential Item Functioning (DIF)

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• 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)

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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.

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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 .

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Step 2 Determine Cutoffs

• (here three groups percentiles 33 and 67).
• EXAMINE
• VARIABLESsumscore /PLOT BOXPLOT STEMLEAF
• /COMPARE GROUP /PERCENTILES(33, 67) HAVERAGE
• /STATISTICS DESCRIPTIVES /CINTERVAL 95
• /MISSING LISTWISE /NOTOTAL.
• OR
• FREQUENCIES
• VARIABLESsumscore
• /NTILES 3
• /ORDER ANALYSIS .

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Step 3 Compute Level

• RECODE
• sumscore
• (Lowest thru 481) (49 thru 572) (58 thru
  Highest3) (ELSESYSMIS)
• INTO level .
• VARIABLE LABELS level 'Score level'.
• EXECUTE .

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Step 4 Carry out ANOVAs

• 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 grouplevel .
• 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 gt .06 Cohens medium effect size)

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Regression

• DESCRIPTIVES VARIABLESsumscore cult
• /STATISTICSMEAN STDDEV MIN MAX.

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• compute predictor values for these new
  variables.
• compute dev_meansumscore-52.6091.
• compute dev_cultcult-1.4473.
• EXECUTE .
• compute interaction dev_meandev_cult.
• EXECUTE .

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• REGRESSION
• /MISSING LISTWISE
• /STATISTICS COEFF OUTS R ANOVA
• /CRITERIAPIN(.05) POUT(.10)
• /NOORIGIN
• /DEPENDENT i_acad_1
• /METHODENTER sumscore
• /METHODENTER cult
• /METHODENTER interaction.

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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)

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Framework of Acculturation Acculturation
Variables

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

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

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

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• 2 examples
• Perceived acculturation context
• Perceived cultural distance

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Structure of Perceived Environment
Mainstream context
97
Minority context
98
Role of (perceived) cultural distance
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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 maintenance
• maintaining characteristics of own
  (heritage) culture
• Cultural adoption
• adopting characteristics of the culture of the
  society of settlement

104
Acculturation Models
105
Berrys Bidimensional Model
Yes
Separation
Integration
Cultural maintenance?
No
Assimilation
Marginalization

Yes
No
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

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Assessment of AcculturationRecurrent 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 AcculturationRecurrent Problems
(contd)

• 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)
• Measure of only adoption dimension, not of
  maintenance dimension

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

• 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

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(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 Strategies
7 6 5 4 3 2 1
Private
Public
Cultural maintenance (Tu)
1 2 3 4 5 6
7
Cultural adoption (Du)
122
Summary of Results
Results of the one-statement and the
two-statement measurement methods domain
specificity
7 6 5 4 3 2 1
Private
Public
Cultural maintenance (Turkish)
1 2 3 4 5
6 7
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 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?
124
(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
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 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)
134
A C C U L T U R A T I O N
M E A S U R E M E 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