Quantitative Resilience
Research across Cultures
and Contexts
Fons J. R. van de Vijver
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
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 crosscultural 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

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
 1 and 2: the reliabilities (usually Cronbach's
of an instrument in two cultural groups.
 Statistic (1-12) follows an F distribution
with N1 - 1 and N2 - 1 degrees of freedom (N1 and
N2 are the sample sizes).
Problem 3

Samples show confounding
differences
• 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
• 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
• 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
Type
Source
Construct bias
Theoretical construct
Method bias
Measurement aspects
(e.g., sample, test,
administration)
Specific item aspects
(e.g., poor translation)
Item 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 nonWestern countries.
• Definition of happiness in
individualistic and collectivistic
countries?

Example: Uchida, Norasakkunkit and
Kitayama (2004):
Types and Sources of Method Bias
Type
Source
Sample bias
Confounding sample differences (e.g.,
education)
Instrument bias Test characteristics (e.g., scoring of
open end responses, response sets)
Administration Procedural aspects (e.g., interviewer
bias
effects, lack of standardization of
administration)
Method bias tends to have a global influence on crosscultural 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
1
Mean score
0.8
0.6
Culture A
Culture B
0.4
0.2
0
1
2
3
4
Total test score
5
6
Types of (un)biased items
(b) Item with uniform bias
5
5
4
4
Mean score
Mean score
(a) Unbiased item
3
2
1
3
2
1
0
0
Very low
Low
Medium
High
Very low
Very high
Low
Culture A
Culture B
(c) Item with non-uniform bias
Very high
Mean score
4
3
2
1
0
Culture B
(d) Item with both uniform and nonuniform bias
5
5
Mean score
High
Score level
Score level
Culture A
Medium
4
3
2
1
0
Very low
Low
Medium
High
Very high
Very low
Low
Score level
Culture A
Culture B
Medium
High
Score level
Culture A
Culture B
Very high
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
Types of Equivalence
Three types:
•1. “Structural” or “functional
equivalence”
•2. “Metric equivalence” or
“measurement unit
equivalence”
•3. “Scalar equivalence” or
“full score equivalence”
(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
(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)
(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
Comparability and Equivalence
Levels
Equivalence
Comparability
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?


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

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
Sample
age
6
7
8
9
10
11
12
13
14
15
16
male
female
Belgium/Neth
Canada
Belgium/Neth
Canada
56
50
54
50
46
50
56
50
66
50
58
50
55
50
53
50
59
50
60
50
53
50
60
50
47
50
57
50
48
50
53
50
62
50
63
50
53
50
56
50
54
50
56
50
12 Subtests
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Picture Completion
Information
Coding
Similarities
Picture Arrangement
Arithmetic
Block Design
Vocabulary
Object Assembly
Comprehension
Symbol Search
Digit Span
Analysis Steps
1.
2.
3.
Determine number of factors in
combined sample
Carry out factor analyses per group
Compare factors across groups
Note: analysis of scaled scores
1. Determining Number of
Factors
Scree Plot
6
5
4
3
Eigenvalue
2
1
0
1
2
3
4
Component Number
5
6
7
8
9
10
11
12
13
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
2. Factor Analyses per group:
Oblimin-Rotated Solution
country = Belgium/Neth
Scaled score for picture completion
Scaled score for information
Scaled score for coding
Scaled score for similarities
Scaled score for picture arrangement
Scaled score for arithmetic
Scaled score for block design
Scaled score for vocabulary
Scaled score for object assembly
Scaled score for comprehension
Scaled score for symbol search
Scaled score for digit span
Scaled score for mazes
Component
1
0.15
0.79
-0.02
0.77
0.10
0.58
0.06
0.87
-0.05
0.78
-0.02
0.37
-0.10
2
-0.05
0.03
0.92
0.02
0.13
0.13
0.12
-0.03
0.00
-0.06
0.85
0.14
-0.07
3
-0.73
0.00
0.04
-0.10
-0.61
-0.10
-0.62
0.00
-0.76
-0.05
-0.10
0.17
-0.19
4
-0.16
0.07
-0.05
-0.06
-0.04
0.16
0.27
0.02
0.16
-0.11
-0.02
0.54
0.82
2. Factor Analyses per
group: Oblimin-Rotated
Solution
country = Canada
Scaled score for picture completion
Scaled score for information
Scaled score for coding
Scaled score for similarities
Scaled score for picture arrangement
Scaled score for arithmetic
Scaled score for block design
Scaled score for vocabulary
Scaled score for object assembly
Scaled score for comprehension
Scaled score for symbol search
Scaled score for digit span
Scaled score for mazes
1
0.38
0.86
-0.09
0.82
0.34
0.52
0.11
0.92
0.14
0.74
-0.01
0.31
-0.13
2
0.04
-0.09
0.94
0.00
0.26
0.13
0.27
-0.05
0.11
0.07
0.83
0.21
-0.11
3
-0.37
-0.02
0.10
-0.03
-0.15
-0.25
-0.62
0.07
-0.57
0.09
-0.07
-0.14
-0.88
4
-0.31
0.08
0.05
-0.04
-0.37
0.32
-0.09
0.03
-0.30
-0.01
0.03
0.69
0.19
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)
3. Compare Factors across
Groups

Formula (x and y are loadings after
target rotation of one to the other):


2
2
 xi  yi
xi yi
3. Compare Factors across
Groups
original
sum
multiplication
-0.2
0.2
-0.4
0.2
0.6
0.4
0.3
0.7
0.6
0.4
0.8
0.8
phi
0.858474
1
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
3. Compare Factors across
Groups


Dedicated software needed to
compute Tucker’s phi
SPSS routine available
pictcomp
informat
coding
similari
pictarra
arithmet
blockdes
vocabula
objecass
comprehe
symbsear
digitspa
mazes
FACTOR LOADINGS AFTER TARGET ROTATION
0.31
0.07
-0.38
-0.59
0.77
0.02
0.03
0.21
-0.06
0.9
0.13
0.12
0.78
0.03
0.04
0.05
0.22
0.22
-0.38
-0.4
0.57
0.14
-0.12
0.17
0.16
0.2
-0.6
-0.2
0.85
-0.04
0.06
0.17
0.09
0.12
-0.64
-0.42
0.78
-0.05
0.11
0.03
-0.03
0.86
0
0.04
0.28
0.08
-0.23
0.58
-0.12
-0.08
-0.73
0.41
Belg./Neth. rotated
DIFFERENCE
-0.07
-0.09
0.04
-0.05
-0.12
0.05
0.05
-0.07
-0.05
0.04
-0.02
-0.04
0.01
IN LOADINGS AFTER TARGET ROTATION
0.04
-0.01
-0.28
0.11
0.05
0.13
-0.04
0.03
0.07
0.03
0.08
0.09
-0.03
-0.22
-0.03
0
0.12
-0.14
-0.06
0.01
-0.11
0.02
-0.01
0.15
0
-0.07
-0.12
-0.12
0.02
0.04
0.03
0.07
0.01
-0.14
-0.09
-0.11
0.02
0.15
0.22
PROPORTIONALITY COEFFICIENT per Factor:
.99
.98
.97
.91
Conclusion


Strong evidence for similarity of first
two factors
Less convincing for third and fourth
factor
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
Item
Dimension
Making sure this country has strong
Materialism
defense forces
Seeing that people have more to say
Postmaterialism
about how things are done at their jobs
and in their communities
Trying to make our cities and countryside
Postmaterialism
more beautiful
Maintaining order in the nation
Materialism
Giving people more to say in important
Postmaterialism
government decisions
Protecting freedom of speech
Postmaterialism
A stable economy
Materialism
Progress toward a less impersonal and
Postmaterialism
more humane society
Progress toward a society in which ideas
count more than money
Postmaterialism
Pooled solution
Item
Defense
Within
-.30
Democracy1
.56
Cities
.02
Order
-.67
Democracy2
.57
Free speech
.31
Econ. Stab.
-.63
Humane
.54
Ideas
.43
Eigenvalue (percentage explained)
2.14
(23.9%)
(Sign of loadings in line with expectation)
Stem-and-Leaf Display of Agreement Pooled
Loadings and Factor Loadings per Country
Stem Leaf
.99
01346
.98
00012566667
.97
56789
.96
36
.95
068
.94
227
.93
249
.92
4
.91
8
.90
348
.89
1
.57 (Extreme)
Each leaf represents one observation (country)
Correlations of GNP and the Loadings per
Region on the Postmaterialism Scale
Item
Correlation
Defense
.06
Democracy1
-.26
Cities
.51**
Order
.59***
Democracy2
-.60***
Free speech
-.09
Econ. Stab.
-.50**
Humane
.52***
Ideas
.47**
Conclusion:
Postmaterialism concept
more salient in more
affluent countries
Metric Equivalence
at Scale Level:
Structural Equation
Modeling
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
Basic Model
e1
1
INFORMAT
1
e2
e3
e4
e5
1
1
1
1
SIMILARI
ARITHMET
VOCABULA
COMPREHE
intelligence
1
e6

Use of multiple group option



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.
AMOS model
e1
1
INFORMAT
1
e2
1
a
SIMILARI
b
e3
1
ARITHMET
c
intelligence
1
d
e4
e5
1
1
VOCABULA
COMPREHE
Measurement weights
e6
AMOS model
e1
1
INFORMAT
1
e2
1
a
SIMILARI
b
e3
1
ARITHMET
c
intelligence
1
e6
d
e4
e5
1
1
VOCABULA
COMPREHE
Structural residuals
AMOS model
e1
1
INFORMAT
1
e2
1
a
SIMILARI
b
e3
1
ARITHMET
c
intelligence
1
d
e4
e5
1
1
VOCABULA
COMPREHE
Measurement residuals
e6
BelgNeth Unconstrained
Estimate
S.E.
C.R.
P Label
Regression Weights: (Canada - Unconstrained)
COMPREHE
<---
intelligence
.952
.042
22.661
*** a1_1
VOCABULA
<---
intelligence
1.144
.043
26.736
*** a2_1
ARITHMET
<---
intelligence
.801
.036
22.415
*** a3_1
SIMILARI
<---
intelligence
1.031
.042
24.720
*** a4_1
INFORMAT
<---
intelligence
1.000
Estimate
S.E.
C.R.
Canada
P Label
COMPREHE
<---
intelligence
.874
.040
21.770
*** a1_2
VOCABULA
<---
intelligence
1.158
.041
28.323
*** a2_2
ARITHMET
<---
intelligence
.780
.038
20.796
*** a3_2
SIMILARI
<---
intelligence
1.056
.039
26.886
*** a4_2
INFORMAT
<---
intelligence
1.000
CMIN
Model
Unconstrained
Measurement weights
Structural residuals
Measurement residuals
Saturated model
Independence model
NPAR
22
18
CMIN DF
P CMIN/DF
47.982 8 .000
5.998
51.793 12 .000
4.316
17
53.049 13 .000
11
66.732 19 .000
30
.000 0
10 5084.104 20 .000
4.081
3.512
254.205
RMR, GFI
Model
Unconstrained
Measurement weights
RMR
.157
.185
GFI AGFI PGFI
.992 .970 .265
.991 .978 .397
Structural residuals
.241 .991
Measurement residuals .227 .988
Saturated model
.000 1.000
Independence model
4.034 .450
.979
.982
.429
.626
.175
.300
RMSEA
Model
Unconstrained
Measurement weights
Structural residuals
Measurement residuals
Independence model
RMSEA LO 90 HI 90 PCLOSE
.046 .034 .059
.658
.038 .028 .049
.969
.036
.033
.330
.027
.025
.322
.047
.042
.338
.985
1.000
.000
Nested Model Comparisons
Assuming model Unconstrained to be correct:
Model
DF
CMIN
P
Measurement weights
Structural residuals
Measurement residuals
4
5
11
3.811
5.067
18.750
.432
.408
.066
NFI
Delta-1
.001
.001
.004
IFI
Delta-2
.001
.001
.004
RFI
rho-1
-.007
-.008
-.010
TLI
rho2
-.007
-.008
-.010
NFI
Delta-1
.000
.003
IFI
Delta-2
.000
.003
RFI
rho-1
-.001
-.003
TLI
rho2
-.001
-.003
NFI
Delta-1
.003
IFI
Delta-2
.003
RFI
rho-1
-.002
TLI
rho2
-.002
Assuming model Measurement weights to be correct:
Model
Structural residuals
Measurement residuals
DF
CMIN
P
1
7
1.256
14.939
.262
.037
Assuming model Structural residuals to be correct:
Model
Measurement residuals
DF
CMIN
P
6
13.683
.033
Metric Equivalence
at Item Level:
Item Bias Analysis/
Differential Item
Functioning (DIF)


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 intervallevel dependent variables (e.g.,
Likert-scale), one variable
indicating culture.
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 .
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 .
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 .
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 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)
Regression
DESCRIPTIVES VARIABLES=sumscore
cult
/STATISTICS=MEAN STDDEV MIN
MAX.
* 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.
EXECUTE .
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 firsthand 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)
Sociocultural
competence in
ethnic culture
(interaction with
conationals,
maintenance of
culturally appropriate
skills and behaviors)
Sociocultural
competence in
mainstream
culture
(interaction with
hosts, acquisition of
culturally appropriate
skills and behaviors)
Characteristics of
the society of
origin (objective,
perceived)
Characteristics of
the immigrant
group (objective,
perceived)
Personal
characteristics
Cultural
maintenance
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
102

Cultural maintenance
• maintaining characteristics of own
(heritage) culture

Cultural adoption
• adopting characteristics of the
culture of the society of settlement
Acculturation Models
 Unidimensional model
Cultural
maintenance
 Bidimensional model
Cultural maintenance
Cultural adoption
Cultural
adoption
Berry’s Bidimensional Model
Yes
Separation
Integration
Marginalization
Assimilation
Cultural
maintenanc
e?
No
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)

Measure of only adoption dimension, not of
maintenance dimension
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)
 I find it important to have
 only Turkish friends.
 more Turkish than Dutch friends.




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)
Turkish
private
public
 Recommendation
 This method is often quite useful
in practice, despite conceptual problems
 Take domains into consideration
115
Dutch
(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
(1,7)
Separation
6
(7,7)
Integration
Private
Public
5
Cultural
maintenance
(Tu)
4
3
2
1
Marginalization
(1,1)
1
2
3
Assimilation
(7,1)
4
5
6
Cultural adoption (Du)
7
Summary of Results
Results of the ‘one-statement’ and the
‘two-statement’ measurement methods:
domain specificity
Turkish
Private
Dutch
Public
7 (1,7)
Separation
6
(7,7)
Integration
Private
Cultural
Public
5
maintenance 4
(Turkish)
3
2 Marginalization
1 (1,1)
1
2
3
Assimilation
(7,1)
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
but I find it important to have Turkish friends.
1 2 3 4 5
 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
methods
Results
 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 fourstatement 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)
One-item method
Public domain
.26#
-.81
One-item method
Private domain
.48$
.54
A
Public
C
-.80
domain
C
U
Two-item method
Turkish culture
Private domain
-.26#
.88$
L
.55$
T
Two-item method
Dutch culture
Public domain
U
R
-.88$ .67
##
A
.75
T
I
O
N
Two-item method
Turkish culture
Public domain
-.85
Private
Two-item method
Dutch culture
Private domain
-.70
.69
M
#
E
.67$
A
S
.43
U
.68
.24
Two-item
measurement
method
.65#
R
E
M
E
Four-item method
Integration
Private domain
Four-item method
Separartion
Public domain
T
.89$
Four-item method
Separation
134
Private domain
.96$
.31
-.32
#
.32
.12#
N
Four-item method
Integration
Public domain
domain
One-item
measurement
method
-.06
Four-item
measurement
method
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