Online Technical Appendix for:
Mindscapes across Landscapes: Archetypes of Transnational and Subnational Culture [query 3]
The Technical Appendix provides additional details about: (1) Measurement of Schwartz values in
WVS 2005, (2) Steps to preprocess the WVS 2005 data, (3) Testing heterogeneity in country values,
(4) The archetypal analysis (AA) algorithm, comparisons with other methods and
advantages/limitations, (5) Applying AA to our data, and the stability and validity of our solutions, (6)
Tests of response bias, and (7) Tables of sample statistics. For all these analyses and tests we use
significance levels of 0.005 and 0.001 rather than the more common 0.05 and 0.01. This is partly
because of the large sample sizes in the WVS but also because of recent recommendations on
standards for statistical evidence. In these recommendations, 0.005 is seen as the minimum level for
claiming significance and 0.001 as denoting “highly significant” results (Johnson, 2013).
(1) MEASUREMENT OF SCHWARTZ VALUES IN WVS 2005
WVS 2005 measures the ten Schwartz values with an importance rating on a 1 to 6 scale. First, the
interviewer explains the procedure to the respondent. This procedure is for them to describe some
people and to ask whether the respondent considers that person is like them. The interviewer hands the
respondent a card with a verbal description of each scale point to facilitate this process (1 = “Very
much like me”, 2 = “Like me”, 3 = “Somewhat like me”, 4 = “A little like me”, 5 = “Not like me”, 6 =
“Not at all like me”). The interviewer then reads the respondent a statement, for example “it is
important to this person to be rich, to have a lot of money and expensive things” and asks them to
indicate from the card whether this person is like them or not. This procedure is then repeated for the
other nine items in the Schwartz values (see Table A1). Thus, in common with most research on
values, the WVS obtains a measure of how important this value is to the respondent (each description
contains the words “it is important to this person”). Across the ten representative items we therefore
obtain a profile of which values the respondents regard as more or less important to them. Although it
is possible to envisage more sophisticated measurement approaches to this simple importance rating
(for example, rank-logit or conjoint approaches, Wedel & Kamakura, 2000: Chapter 16) the WVS
contains a large number of questions on a broad variety of topics. The simple rating is therefore a
trade-off between the time available for these questions and the challenge of administering them face1
to-face in many settings around the globe. Finally, for ease of interpretation, we reverse this scale in
our data so that in this article, 6 means “Very much like me” and therefore important, and 1 means
“Not at all like me” and therefore unimportant.
==============================
TABLE A1 ABOUT HERE
==============================
Schwartz often represents his values model by arraying the motivational types around a circle
(Schwartz, 1992). This we show in Figure A1 for the ten values of interest here. The right side of the
circle represents the values of self-transcendence and conservation, whereas the left side reflects selfenhancement and openness to change values. For clarity of explanation we also show on this figure an
example of what we mean by an archetype, namely a specific configuration of values held by a group,
i.e., the perfect example of the group’s values. This we represent by the bold line on the figure. This
archetype is hypothetical and shows a cultural group that is strong on universalism, benevolence,
tradition, conformity and security, i.e., oriented to self-transcendence/conservation values rather than
self-enhancement/openness to change values.
==============================
FIGURE A1 ABOUT HERE
==============================
The World Values Survey adopts multiple strategies to overcome the potential problems of bias
in cross-cultural surveys. One, the survey contains over 250 questions. The size, complexity and
diversity of the questionnaire make it difficult for respondents to have a systematic bias in their
responses (Baumgartner & Steenkamp, 2001). Two, the survey contains both nominal and interval
scales, with each scale containing two to ten response categories. Three, the scale anchors are mixed,
both in wording and direction, making it difficult for respondents to answer questions in a biased
manner: “The advantage of balanced scales is that they have a built-in control for stylistic responding
because a high (low) score cannot be obtained simply because of yea-saying (nay-saying)”
(Baumgartner & Steenkamp, 2001: 144). Four, according to Fisher (1993: 303), “an important
technique used by researchers to mitigate the effects of social desirability bias is indirect (i.e.,
2
structured, projective) questioning.” The World Values Survey uses a projective technique to
overcome social desirability bias in answering the Schwartz values questions. For example, the
interviewer asks the following question before presenting the ten Schwartz values questions: “Now I
will briefly describe some people. Would you please indicate for each description whether that person
is very much like you, like you, somewhat like you, a little like you, not like you, or not at all like
you?” Thus, instead of directly asking the respondents about their motivational values, the survey asks
respondents if they are similar to or different from another person with a specific values profile.
Finally, any biases that do exist are inherent to the methodology of the WVS which, whatever its
shortcomings, remains the only publicly available source of large samples of individual level values
data from multiple countries.
These comments notwithstanding, we also conducted some tests of response biases in our WVS
data, which we report in section 6 of this Technical Appendix. There we find any such biases as exist
are likely to be small, demonstrating that WVS data is adequate for our purposes here.
(2) STEPS TO PREPROCESS THE WVS 2005 DATA
We include in our data from WVS 2005 the ten Schwartz values plus 43 other variables including
demographics, values, attitudes and perceptions. We use demographic variables for our tests of
heterogeneity, and all variables to examine relationships with our archetypes. While the quality of the
WVS 2005 data is generally high, preprocessing is necessary to prepare it for analysis. We do this in
three steps and separately for each of the four countries. First, we drop respondents who did not
answer many of the Schwartz value statements. This is necessary for the imputation model we use in a
subsequent step to work effectively. This is a relatively minor problem for Japan, the USA and China
where we only drop 2% to 3% of the respective samples, but a more pronounced one for India where
some 12% of respondents did not answer half or more of the Schwartz questions. Second, we do a
preliminary imputation of missing values and then use an appropriate test to eliminate outliers. Like all
statistical models, both our imputation and archetypal analysis methods are vulnerable to outliers. We
discuss the details of this step subsequently as appropriate outlier tests are necessary for archetypal
analysis. Third, we re-impute missing values for the remaining cases, which range from 85% to 98%
3
of the original country samples. Table A2 provides a summary of sample sizes at each step in this
preprocessing.
==============================
TABLE A2 ABOUT HERE
==============================
Missing Values and Imputation
Although most of the cases in our final sample have complete data on all these variables, some contain
missing values as is common in survey research. For individual variables, the amount of missing data
ranges from 0% to 19%, though most variables have only 2% to 5% missing. The average data
missing per variable is 2.9% for Japan, 2.8% for the USA, 3.0% for China and 3.8% for India. The
variable and countries with the most missing data are income for China (19%) and the value of
universalism for India (13%). Overall, of the 5,906 questionnaires in our final database 1,353, or 23%,
have one or more values missing across the set of focal variables. The percentage of incomplete cases
per country is 27% for Japan, 7% for the USA, 28% for China and 26% for India. Dropping these
cases could potentially introduce serious bias into our results (Rubin, 1987). Best practice in social
science therefore recommends imputation as the approach to follow in such circumstances (Graham,
2009). Here we use a bootstrap expectation maximization method to impute the missing values
(Honaker, King & Blackwell, 2011). Following the guidance from recent simulation studies on how
many imputations are necessary, we run 30 imputations corresponding to the higher missing data in
Table A2 (White, Royston & Wood, 2011).
Various diagnostic tests on these imputations produce acceptable results. First, for all four
countries there are many patterns to the missing data, suggesting no systematic patterns to these
missing values. Second, the expectation maximization algorithm converges normally to the same
solution from several different starting points. Third, separate plots for each of the variables show that
the vast majority of imputed values fall within the expected confidence limits across the range of the
variable (the “overimpute” test of Honaker, King & Blackwell, 2011). Fourth, the standard errors of
imputation (Rubin, 1987) across the 30 replicate databases are small, especially on overall statistics
such as means, where they are approximately 0.01 of scale point (1 to 6 scale) for the ten Schwartz
4
values and the four countries on which we base our archetypal analyses. The standard errors are higher
for the archetypes themselves, as might be expected because we are estimating multiple coordinates
from the data rather than estimating a single point. However, they are still small, ranging from 0.1 to
0.3 of a scale point (1 to 6 scale) across all values, archetypes and countries. There is one exception to
this conclusion, archetype F for India, which we will discuss later (in section 5, Applying AA to our
data).
Outliers
Outliers are also a potential problem in all statistical analyses, including archetypal analysis. If a
subset of the data comes from a very different population or contains major measurement errors, then
including this subset can create serious bias and even “break down the archetypal solution” (Eugster &
Leisch, 2010: 1). However, for archetypal analysis we have to be more precise on what we mean by
“outlier” as we essentially use the frontier of the data cloud to define our archetypes. Thus we need to
include cases that belong to the same population but exclude cases that belong to other populations.
Therefore standard heuristics to exclude outliers are inappropriate and we need to use techniques
designed to identify cases from other populations. Here first we follow the approach of Rousseeuw
and Leroy (1987) to generate robust estimates of the Mahalanobis distance of each of our respondents
from the center of the cloud. We then use Hardin and Rocke’s (2005) outlier distance statistic and a
probability level of 0.001 to identify extreme outliers, i.e., those that likely belong to a different
population. Hardin and Rocke’s statistic is expressly designed for this purpose. In the end, we identify
and drop 12 outliers from Japan, 1 from the USA, 29 from China and 62 from India. In total, these
outliers represent less than 2% of the original data. To cross check these procedures we also generated
some archetype solutions including these outliers and determined they result in distorted and
unpopulated extrusions of the solution as the algorithm tries to span two populations.
(3) TESTING HETEROGENEITY IN COUNTRY VALUES
One way to test for heterogeneity in values within a country is to compare within and between country
variance on the values measures. If this comparison shows more variance within countries than
between countries, it indicates that within-country heterogeneity is the more important phenomenon.
This is one of the approaches shown in Fischer and Schwartz (2011). The second way is to define
5
subgroups within the country on some independent categorical variable and then test whether the
values these subgroups hold are the same or different. Here we use five demographic variables from
the WVS to define subgroups (age, education, gender, income, and social class). We variously use
three or four subgroups for class and education and quartiles for age and income (reflecting the best
way to categorize the various distributions to obtain roughly equal size subgroups). We then perform
two robust multivariate tests on the heterogeneity of the ten Schwartz values. The first is a one-way
multivariate analysis of variance testing for the equality of subgroup means. Here we use the minimum
covariance determinant algorithm of Todorov and Filzmoser (2010), which provides estimates of
Wilk’s lambda that are more robust than those of classical methods. The second test is through the Estatistic (Szekely & Rizzo, 2013). The E-statistic is also distance-based and tests for the equality of the
multivariate distributions of the various subgroups through non-parametric procedures. If we can show
heterogeneity of values on these variables then there are likely better ways to describe this
heterogeneity than by demographic subgroups. For our fourth and last test of heterogeneity we follow
another of the approaches shown in Fischer and Schwartz (2011). They apply the inter-rater agreement
statistic of Brown and Hauenstein (2005) to establish the degree to which consensus on the importance
of values exists within a country. We do the same here, computing this statistic across each of our
country samples and for all ten Schwartz values. This is not a multivariate approach but does provide a
different perspective and uses the total sample for each country rather than subgroups. The results of
the four tests are summarized below.
Within and Between Country Variance
Consistent with previous studies (e.g., Fischer & Schwartz, 2011; Gelfand et al., 2011; Steel & Taras,
2010), and using the intraclass correlation coefficient, we find that across the ten Schwartz values, the
average percent variance that resides within and between countries is 82% and 18% respectively
(based on ICC(1) from Gelfand et al., 2011). The highest percent within countries is for hedonism at
97.6% and the lowest for tradition at 69.7%. Within country heterogeneity is thus important and
substantial (see Table A3).
==============================
6
TABLE A3 ABOUT HERE
==============================
Multivariate Means and Distributions
Confirming our first finding, the two multivariate tests more formally reject homogeneity in both
means and distributions for all four countries on all five demographic variables – age, education,
gender, income and social class (see Table A4). First, of the 20 tests of the equality of multivariate
means in the table (four countries by five demographic subgroupings in each), 19 reject the null
hypothesis of equal subgroup means, 18 at the probability level of 0.001 (1 in 1,000) and one at the
level of 0.005 (1 in 200). Second, of the 20 tests for equality of multivariate distributions, 18 reject
equality of subgroup distributions, 17 at the probability level of 0.001, and one at 0.005.
==============================
TABLE A4 ABOUT HERE
==============================
Inter-rater Agreement
Table A4 summarizes the average inter-rater agreement for each country over the 10 Schwartz values
and also identifies the individual value with the highest inter-rater agreement amongst these. Low
inter-rater agreement implies heterogeneity; high agreement suggests homogeneity in individual
values within countries. Note this is not a subgroup test; instead it uses all the respondents in each
country as “raters” of the values. This is also not a statistical test and we have to rely on guidance from
researchers in this area as to what is an acceptable level of agreement to claim consensus on the
importance of a value. Brown and Hauenstein (2005), drawing on the work of others, suggest a level
0.80 is needed for strong agreement, 0.70 for moderate agreement, 0.60 to 0.69 for “reasonable”
agreement and levels of 0.59 and below are “unacceptable.” From the table we see the average levels
of agreement are low, Japan being the highest at 0.56 and India the lowest at 0.29. Indeed, of the 40
inter-rater scores that lie behind these averages (four countries by 10 Schwartz values), 38 are
“unacceptable” according to Brown and Hauenstein, “especially if aggregating individual responses to
represent group-level consensus” (Brown and Hauenstein, 2005: 178). Only for Japan and the value of
benevolence is there a “reasonable” degree of consensus. Overall, our four tests show there is large
7
and significant heterogeneity in the values held by the citizens of each of the four countries we
examine here.
(4) THE ARCHETYPAL ANALYSIS (AA) ALGORITHM, COMPARISONS WITH OTHER
METHODS AND ADVANTAGES/LIMITATIONS
This section complements the more conceptual discussion of AA in the main text with some of the
more technical aspects of how the algorithm works. We first discuss the algorithm itself, then how to
determine the number of archetypes. Next, we compare AA with other methods and its potential
advantages and limitations.
The AA Algorithm
The foundation paper for archetypal analysis is Cutler and Breiman (1994). As explained by Eugster
and Leisch (2009), the essential problem AA seeks to solve is as follows. Assume you have a matrix X
of multivariate data with n observations and m variables. Further assume you know the number of
archetypes you wish to generate, k (a point we will return to). Then the algorithm seeks to find a
matrix Z of k m-dimensional archetypes that satisfy two important conditions.
(1) The data are best approximated by the convex combinations of the archetypes that minimize
the residual sum of squares; namely RSS = ||X – αZT||2 where the coefficients α are >0 and sum
to 1 across the k archetypes. These α coefficients (n by k) are those that associate each case
with each archetype and which we use to differentiate between our “archetypal” and “nonarchetypal” cases.
(2) The archetypes themselves are convex combinations of the data points; namely Z = XTβ and
the coefficients β are also > 0 and sum to 1 across the n rows.
The way these equations are solved to satisfy the two conditions is by an optimization procedure that
alternates between finding the best α’s for a given set of archetypes and finding the best archetypes for
a given set of α’s (Cutler & Breiman, 1994: 345). At each step, the algorithm needs to solve several
convex least squares problems and linear equations. There is more than one approach to solving these
equations and the package we employ uses a penalized least square approach (Eugster & Leisch,
2009). Cutler and Breiman (1994) show that the AA algorithm always converges to a minimum but
8
not always the global minimum. Thus the researcher has to try more than one starting points for the
algorithm in order to be confident they have found the best fit.
Determining the Number of Archetypes
Given we can generate a solution for a given number of archetypes, k, how do we choose between
different ks? In fact, this is done in the same way as for several other statistical methods, namely by
examining how the residual sum of squares decreases as the number k increases and determining
where this improvement shows an elbow. In factor analysis this is called a “scree plot.” At present
there is no other criterion such as AIC or BIC that has been developed for AA. The challenge in
developing such a statistic is that the error structure resulting from estimating the joint coordinates of a
set of archetypes is obviously different to that of fitting, say, a regression.
Comparison of AA with Other Methods
There are several similarities between AA and other methods such as cluster or latent class analysis.
All three methods use iterative optimization techniques to fit their models to data and all three produce
multiple profiles across the variables of interest. All three require running from multiple start points to
ensure best fit and all three require comparisons of fit at different numbers of archetypes, classes and
clusters to choose the overall solution. There are also some straightforward differences. Cluster
analysis is perhaps more often used with interval-level data and latent class analysis with categorical
data. AA assumes interval-level data like most forms of cluster analysis.
But there are also two further and more striking differences between AA and the other methods.
The first of these relates to objectives, the second to philosophy concerning the information content of
a sample case. On objectives, most forms of cluster and latent class analysis seek to divide the sample
into distinct subgroups (“clusters”, “classes”). In contrast, AA seeks to describe each sample case as a
weighted combination of a small number of archetypes, with each archetype being a “perfect
example” of a subgroup of configurations within the overall data. This leads directly to the second,
philosophical, point. AA effectively treats cases on the frontier of the multidimensional data cloud as
more informative than cases in the middle of this cloud. It is from these frontier cases the archetypes
are derived. The fact the archetypes represent data in the annular region surrounding the average
middle derives from the way the mathematical problem is set up (Eugster & Leisch, 2009). Cutler &
9
Breiman also formally prove that if there are N data points that define the convex hull of the data,
there are k (< N) archetypes that minimize the residual sum of squares between all the cases (as
weighted combinations of archetypes) and the original data (Cutler & Breiman, 1994: 344). In
contrast, cluster and latent class analysis treat each case equally regardless of its position in the cloud.
The bottom-line is that AA effectively considers the topology of the whole sample; the other methods
consider a more restricted local topology around their clusters or classes. In that sense, these methods
may not be fully comparable.
Advantages and Limitations of AA in Comparison with Other Methods
Note we are not arguing here that one method is superior to any other; this clearly depends on the
researcher’s objectives and data. Equally all three methods, cluster analysis, latent class analysis and
AA come not only with specific advantages but also limitations. Moreover, cluster analysis dates from
the 1930s and latent class analysis from the 1960s, and as a consequence both have an extensive
literature for researchers to draw on. Hence, the advantages and limitations of these two methods are
better known than is the case for AA, which dates from the 1990s and thus has a smaller literature.
What is discussed in the AA literature is that some authors argue it produces sharper solutions than
cluster analysis (e.g., Elder & Pinnel, 2003) and through simulation studies others show AA is robust
to various types of noise in the data (e.g., Chan, Mitchell & Cram, 2003). Some authors also point out
that AA does not impose a strong model or set of external assumptions on the data. In particular, it
does not impose the sorts of artificial orthogonality constraints that underlie several cluster methods
(Li, Wang, Louviere & Carson, 2003).
The limitations of AA mentioned in this literature include the fact that the chance of finding
local (sub-optimal) fits to the data increase with the number of archetypes (Cutler & Breiman, 1994).
However, this is also true for other statistical techniques using optimization methods such as some
types of cluster analysis and also latent class analysis, which is particularly prone to such problems
(Goodman, 1974) as well as those of local dependence amongst variables (Hagenaars, 1988). More
AA specific limitations come from our own experience here, of which we regard two as important to
note. First, AA is relatively computing intensive; requiring powerful computers if run times are to be
kept in a practical range (Intel i7 or the use of multiple cores through parallel computing techniques).
10
This computing power allows the number of replications from different start points that are necessary
for the researcher to be confident they have a good fit to the data. It also allows analyses to be repeated
if multiple imputations are necessary because of missing data. Second, relatively large samples are
preferable so that single data points do not carry disproportionate influence on the eventual solution.
Also so each archetype is associated with a reasonable number of cases lending overall stability to the
solution (here we use the heuristic of a minimum of 20 cases per archetype). If the non-archetypal
cases constitute around 50% of the sample, as in our data, we would consider the minimum sample
size for a solution with five archetypes to be 200 but a more conservative number might be 500. These
numbers are not too dissimilar to recommendations for other statistical techniques such as factor
analysis or structural equation modeling. While our conclusions obviously need further investigation,
perhaps through simulation studies, they are not a concern here as our smallest sample size is 1,062.
Notwithstanding these limitations, we believe AA better suits our purpose here, not only through its
theoretical ability to produce a parsimonious and distinct set of value configurations that fully describe
the Schwartz values data, but also because we can show empirically the resulting archetypal groups
have explanatory power over independent variables.
(5) APPLYING AA TO OUR DATA, AND THE STABILITY AND VALIDITY OF OUR
SOLUTIONS
We use the R package “archetypal analysis” which uses robust statistical methods (Eugster & Leisch,
2009). We should note this package does internally standardize the data to the robust statistic
equivalents of mean and standard deviation. However, this standardization should not be confused
with those for response bias discussed in the main text, rather it is simply the normal standardization
applied to the whole sample and to ensure each variable gets equal weight in the analysis. Equal
weighting is also an appropriate strategy for these data as there is no reason we should give one
Schwartz value more importance than another in our analysis. We now describe our procedures for
applying this package to our data.
Basic Procedures
We should note that we repeat the following process for each country separately and then for the final
analysis on pooled data from all four countries. For the latter we take a random sample of 1,000 cases
11
from each country – this is to give each country equal weight in the formation of archetypes, rather
than allowing the larger samples from China and India to overly influence the outcome. We checked
that these samples of 1,000 satisfactorily match the means and standard deviations of the original
country samples on the Schwartz values, which they do. Given this preamble, our basic procedures are
as follows.
First, we use the respondent scores in each country on the 10 Schwartz values as inputs to our
AA. Second, we examine AA solutions from one to ten archetypes, repeating each analysis from 100
random starting points to avoid the problem of local minima and identify the best fit between solution
and data. Third, we repeat the above two steps for each of our 30 imputations, check for consistency of
results across these imputations, and then combine them into the results we report here. Fourth, we
generate 100 databases of normally distributed random numbers, each with the same number of cases
and variables, and the same scale range, as the actual data. We use these random databases to identify
a 1% confidence level on the level of fit we can achieve by chance alone. We compute this confidence
level by applying AA to the random data in the same way as we do for the real data. Figure A2 shows
an example of the best fit indices we observe across our 30 imputations (solid line), the worst fit we
observe across these imputations (dotted line) and the 1% confidence level we generate from random
data (dashed line). Figure A2 is for Japan. The figure demonstrates (1) there is little variation in fit
between best and worst fit imputations, and (2) solutions with four to ten archetypes are clearly
distinguishable from chance. Plots for the USA and China are very similar to Japan; the plot for India
is different in that it has “elbows” at three and six archetypes. The plot for the pooled data is similar to
Japan, the USA and China except the elbow is at five archetypes, not four.
==============================
FIGURE A2 ABOUT HERE
==============================
We use four criteria to choose the number of archetypes in our data. (1) The fit index itself,
particularly those for the elbows in the plot. (2) Whether this fit is better than that we see at the 1%
confidence level for random data. (3) Each archetype is well supported by the data, i.e., it associates
with a reasonable number of cases at scores of 0.5 or better. Here we use a minimum heuristic of 20
12
cases; although 100 or more cases support most of the archetypes in our results for the four countries
and 195 or more cases in the pooled data. (4) Parsimony, i.e., we represent the data with a few, welldifferentiated archetypes. On the basis of these criteria, we chose four archetypes as the best solution
for Japan, the USA and China. For India we chose six archetypes over three because of the great
diversity of this country and because of the better fit of the solution for six archetypes. The archetypes
we identify are the configurations that best describe the data for each country in the ten-dimensional
Schwartz values space. For the pooled data we chose five archetypes to describe the four-country data
in the Schwartz values space.
Stability of Solutions
We should also note the solutions for archetypes we obtain from different imputations are highly
similar to each other. Hence when we combine them, the archetype configurations we show in the
results section of the article have only small standard errors of imputation (we compute these using the
methods of Rubin, 1987). Across all the 23 archetypes we will present, the median standard errors of
imputation for the ten Schwartz values range from 0.17 to 0.29 scale points (1 to 6 scale), with an
overall median of 0.22. Taking the latter number would mean that an important value in a
configuration, say 5, would have an error of 0.22/5 or 4%. An unimportant value in a configuration,
say 2, would have an error of 11%. Naturally these are overall statistics and individual archetypes vary
in the precision of their measurement. However, for 22 of 23 archetypes, the imputation method we
use to address the missing data in the WVS provides highly stable results with low standard errors.
The only exception is archetype F, one of the six archetypes for India. This archetype associates with
only 26 “archetypal” cases and therefore its configuration has higher standard errors of imputation,
averaging 1.2 scale points (1 to 6 scale). We debated whether to retain this archetype. However, it is
only one of six archetypes where the other five have low standard errors of imputation. Also the fivearchetype solution for India has a worse residual sum of squares than the six-archetype solution. We
therefore retain this archetype even though it is less well estimated than the other five in the set for
India.
Validity of Solutions
13
As a final check on the validity of our AA solutions we apply linear discriminant analysis, with the ten
Schwartz values as predictors and membership of the various archetypal plus non-archetypal cases as a
categorical dependent variable. This procedure is a standard way of checking that statistical algorithms
are able to produce good solutions from the data, often used in cluster analysis. If the algorithm
performs its job effectively we should see high rates of classification. The dependent variable has five
categories for Japan, the USA and China, seven for India and six for the pooled data. For each
imputation we run 10 split half predictions (i.e., estimate the linear discriminant model on half the data
and predict category of membership in the other half). We find high levels of correct classification in
all imputations and split-halves. For Japan an average of 83% of the sample are classified correctly
into their respective categories, for the USA 87%, for China 86%, for India 84% and for the pooled
data 86%. All the individual archetypal and non-archetypal cases are also classified well (typically
85% or more). The one exception is the smallest archetype F for India; this has the lowest rate of
correct classification at 67%. Overall, these discriminant analyses provide confidence our archetype
solutions are a valid representation of the Schwartz data on which they are based. Full details of these
split-half predictions are shown in Table A5.
==============================
TABLE A5 ABOUT HERE
==============================
(6) TESTS OF RESPONSE BIAS
Here we first examine yea/nay saying, common method and social desirability biases in the overall
country samples. We then examine the potential for archetype specific biases, including yea/nay
saying, social desirability and country-specific response styles.
Overall Sample Tests
Yea/nay saying bias. To test for this bias, we create a summed index of the ten Schwartz values in
WVS and correlate this index score with 35 other values measured in WVS 2005. Yea or nay saying is
where the respondent always answers either yes/high or no/low without considering the specifics of
the question. Thus correlations between a summed index like this and a broad and disparate set of
other values are one way to test whether respondents are considering the specifics of each question or
14
not. While the majority of the correlations between this index and the other values are positive, which
might indicate possible bias, these correlations are also small. The average correlations are 0.09 for
Japan, 0.07 for the USA, 0.12 for China and 0.17 for India (full details are shown in Table A6). The
average is the appropriate statistic to use because a strong positive average would indicate significant
shared variance between the index and the set of other values. In fact this is not the case, only 0.8%,
0.4%, 1.2% and 3% of the variance is shared between the index and the set of other values. Thus
respondents who systematically answer the Schwartz items high or low do not appear to systematically
answer all the other value questions high or low. The inference here is they appear to be considering
the content of each set of questions. Overall, these small correlations indicate any yea/nay-saying bias
in the WVS data is likely small.
==============================
TABLE A6 ABOUT HERE
==============================
Common method bias. We conduct the Podsakoff and Organ (1986) single factor test for common
method bias in the WVS. Common method bias is where the format of the questionnaire also leads
respondents to answer in one way without considering each item. Principal components analysis of the
ten Schwartz items together with the set of 35 other values did not produce a single dominant
“response bias” factor underlying these variables. Indeed, Horn’s (1965) parallel test indicates that 11
components are necessary to describe these data for each of the four countries, with the first extracted
component accounting for 10%, 10%, 12% and 15% of the total variance for the respective countries.
These results suggest common method bias in the WVS data is also likely to be small – as we might
expect given the many different question formats. Indeed, Podsakoff, MacKenzie, Lee and Podsakoff
(2003) recommend good questionnaire design as the best way to overcome many potential biases and
on the basis of our first two tests the WVS does appear to be well designed.
Social desirability bias. This is where the respondent tries to answer each item in a socially correct
manner rather than give their true answer. Here we form a summed index of the four WVS values
relating to non-ethical behavior that are similar to the items in the well-known social desirability
scales (e.g., Crowne & Marlowe, 1960). We then correlate this social desirability index with two
15
summed indices, one ten-item index as before and a second index where we sum just the five selftranscendence and conservation oriented items in the Schwartz model. The correlations between the
social desirability index and the ten-item index are small as before: 0.04 for Japan, 0.05 for the USA,
−0.07 for China and −0.12 for India. The equivalent correlations for the five-item Schwartz values
index are −0.08, −0.14, −0.17 and −0.20. The negative signs on the latter correlations might be
suggestive of bias, as they imply a respondent who espouses the social values in the Schwartz model
also gives socially correct answers on ethical behavior. However, the magnitude of these effects is also
small, even for the largest (India) only 4% of the variance is shared between the two indices. We
conclude that any social desirability bias in the WVS data is also likely to be small.
Archetype Specific Biases
Yea/nay saying bias. The configurations of archetypes A and D in the pooled analysis, and their
counterparts in the individual country analyses, raise the possibility of a more specific form of yea/nay
saying bias in the WVS data. Archetype A represents respondents who do not regard any of the ten
values as important, and archetype D respondents who regard all ten values as important. Might these
respondents – 28.5% of the sample – be providing systematically positive or negative responses
without thought to the specific questions? To examine the possibility of such bias, we repeat our
earlier test correlating a summed index of the ten Schwartz values with 35 other values. Here we
restrict the test to respondents associated with archetypes A and D. As before, a strong positive
average correlation across the 35 other values would suggest systematic yea/nay saying bias. This is
not what we find. The average correlation is 0.18 and thus only 3% of variance is shared between the
index and other values (full details are given in Table A7). Any systematic yea/nay saying biases in
these archetype configurations are thus likely to be small. We conclude that archetypes A and D are
more likely to represent these individuals’ specific responses to the Schwartz items.
==============================
TABLE A7 ABOUT HERE
==============================
Social desirability bias. Other archetypes raise the possibility of social desirability bias, for example
archetype C. This is a value configuration that gives importance to several values but not to hedonism
16
and power, two values that could be seen as less socially correct. Could it be that these responses are
socially correct ones rather than the true position of the individuals themselves? Here we again use our
four-item index of non-ethical behavior to test for bias. However, we have to adapt our procedures to
compare archetypes rather than look at overall sample statistics as before. In that respect, archetype A
provides a convenient anchor with which to compare the other archetypes. It is difficult to conclude
the respondents represented by archetype A are giving socially correct answers, since they give low
importance to all ten values, and this is not a socially correct stance in most societies. Thus a useful
test is to ask whether the other archetypes score lower (more socially correct) or higher (less socially
correct) than archetype A on this index. In our test we also have to address the issue of statistical
significance. With a sample size of over 2000 (“archetypal” cases, pooled data) even minor group
differences are statistically significant at p < 0.001, but might have little practical meaning. So in this
analysis we focus more on effect size.
For the pooled data (last row in the upper half of Table A8), the archetype index means are A
8.8, B 7.0, C 12.6, D 10.9 and E 8.4 on a scale of 4 to 40 – implying most individuals do not find these
non-ethical behaviors justifiable. The Anova F-statistic is 31.3 and with 4 and 2063 degrees of
freedom this is significant at p < 0.001. The follow-on Tukey HSD test for differences of group means
shows that, while the difference between A and E is not significant at p < 0.001, the others of interest
here are (last row in the lower half of Table A8). Interestingly, archetypes C and D have a higher nonethical index than A, that is, give less socially correct answers than archetype A. However, the effect
sizes for the comparisons of A with B, C and D are all small (−0.32, 0.45 and 0.28 respectively)
meaning these differences, and any potential bias, are of little practical meaning.
We also did these analyses for the individual country archetypes with similar results and
conclusions (see the first four rows in both the upper and lower halves of Table A8). There is only one
significant large effect across the four countries, that for India and the comparison between archetypes
A and B. The other 13 of 14 country comparisons are either not significant (7 comparisons), or where
significant have only small or moderate effect sizes and therefore of little practical meaning. Overall,
as far as we can determine, social desirability bias is relatively small in both county-specific and
pooled archetypes.
17
==============================
TABLE A8 ABOUT HERE
==============================
Country-specific response styles. As we show in the main text, each of the four countries has diverse
archetypes. The best fitting archetypal analysis solutions for Japan, the USA and China have four
archetypes each, and for the most diverse country, India, six archetypes. These archetypes represent
markedly different configurations of values, suggesting that no country has a single or dominant
country-specific response profile. Similarly when we pool the data from the four countries and subject
them to archetypal analysis, the five resulting archetypes are present in all four countries. This further
indicates the absence of country-specific response styles in the WVS data.
(7) TABLES OF SAMPLE STATISTICS
For completeness we include the following tables: A9 “archetype profiles on the ten Schwartz values
for Japan, USA, China and India”; A10 “Schwartz values means and standard deviations for the
archetypal and non-archetypal cases in Japan, USA, China and India”; A11 “distribution of archetypal
and non-archetypal cases by country with pooled data”; and A12 “relationships between pooled
archetypal groups and demographics plus other variables in WVS 2005”.
==============================
TABLES A9 TO A12 ABOUT HERE
==============================
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19
Table A1 Schwartz’s ten motivational value types and representative items in WVS 2005
Motivational Value Type
Representative Item in WVS 2005
1. Universalism
Looking after environment
2. Benevolence
To help the people
3. Tradition
Tradition
4. Conformity
To always behave properly
5. Security
Living in secure surroundings
6. Power
To be rich
7. Achievement
Being very successful
8. Hedonism
To have a good time
9. Stimulation
Adventure and taking risks
10. Self-Direction
To think up new ideas
Source: adapted from Schwartz (1994: 22), Spini (2003: 5) and World Values Survey 2005.
Notes:
Each of the ten value items in WVS 2005 are measured on a six-point scale, with 1 = Very much like
me, and 6 = Not at all like me. The following statement precedes the set of 10 Schwartz values items
in WVS 2005: “Now I will briefly describe some people. Using this card, would you please indicate
for each description whether that person is (1) very much like you, (2) like you, (3) somewhat like
you, (4) a little like you, (5) not like you, or (6) not at all like you?” For example, the item used to
measure the “tradition” motivational value is: “Tradition is important to this person; to follow the
customs handed down by one’s religion or family.” The scales are reversed in our analysis so that high
score means “very much like me”.
Bipolar value dimensions: self-transcendence (universalism, benevolence) versus self-enhancement
(power, achievement, hedonism); and conservation (tradition, conformity, security) versus openness to
change (hedonism, stimulation, self-direction).
20
Table A2 Summary of sample sizes and missing data
Country
Japan
USA
China
India
TOTAL
Original
Sample from
WVS 2005
1,096
1,249
2,015
2,001
6,361
Sample
after check
on Values1
1,074
1,223
1,957
1,756
6,010
Sample after
removing
outliers
1,062
1,222
1,928
1,694
5,906
Final sample
as percentage
of original
97%
98%
96%
85%
93%
Percentage of
incomplete cases
in final sample2
27%
7%
28%
26%
23%
Notes:
1. Removing those who did not answer many of the Schwartz value statements (see text).
2. Percentage of cases with one or more missing on focal variables.
21
Table A3 Within and between country heterogeneitya
Schwartz value
Within
Between
Universalism
89.6%
10.4%
Benevolence
79.7%
20.3%
Tradition
69.7%
30.3%
Conformity
83.5%
16.5%
Security
89.1%
10.9%
Power
76.8%
23.2%
Achievement
77.4%
22.6%
Hedonism
97.6%
2.4%
Stimulation
70.6%
29.4%
Self-direction
85.9%
14.1%
Overall average
82.0%
18.0%
Note: a Percentage of variance, based on the intraclass correlations for the four-country pooled data.
22
Table A4 Heterogeneity tests for Japan, USA, China and India
Demographic
H0: Equality of
multivariate means1
H0: Equality of
multivariate
distributions2
JAPAN
Age
345.2
**5
203.5 **
Class
72.5
**
30.7 **
Education
61.0
**
**
32.7 **
Gender
95.8
Income
65.6
**
122.4
**
142.5 **
Class
39.9
*
31.6 **
Education
91.2
**
**
68.7 **
Income
81.9
**
201.3
**
185.7 **
Class
128.5
**
114.0 **
Education
256.9
**
**
215.4 **
Income
150.1
0.59 Benevolence
0.36
0.53 Benevolence
0.29
0.39 Achievement
27.4 **
**
116.8 **
INDIA
Age
50.9
ns
44.5 ns
Class
183.0
**
135.7 **
Education
123.1
**
99.3 **
41.3
**
14.9 *
Gender
0.43
67.8 **
Age
56.1
0.64 Benevolence
47.0 **
CHINA
Gender
0.56
36.9 ns
Age
108.5
Maximum interrater agreement4
22.9 **
USA
Gender
Average
inter-rater
agreement3
Income
112.1
**
70.0 **
Notes:
1. Robust Wilk’s lambda, statistic is the chi-square approximation (Todorov & Filzmoser, 2010).
2. E-statistic (Szekely & Rizzo, 2013).
3. Average inter-rater agreement (awg(10)) over ten Schwartz values (Brown & Hauenstein, 2005).
4. Maximum inter-rater agreement (awg(1)) observed over the ten Schwartz values/name of value.
23
5. ** = p < 0.001, *=p < 0.005, ns=not significant.
24
Table A5 Archetype validity: split-half predictions from linear discriminant analysis1
Archetypal cases
Sample
A
B
C
D
Japan
USA
China
India
Pooled
912
95
89
87
91
92
93
90
81
92
87
91
91
90
92
85
90
88
89
88
E
85
87
F
67
Nonarchetypal
cases
78
81
81
83
84
Total
83
87
86
84
86
Notes:
1. Linear discriminant analysis with archetypal and non-archetypal membership as the categorical
dependent variable and the ten Schwartz values as independent variables.
2. Statistic is the mean percent correctly classified over 10 split half-predictions.
25
Table A6 Correlations of Schwartz's ten values index with 35 other values in WVS 2005
WVS 2005 Items
Importance of family
Importance of friends
Importance of leisure
Importance of politics
Importance of work
Importance of religion
Happiness
Other people are fair
Satisfaction with life
In control of life
Satisfaction with finances
Need a job to develop your talent
Humiliating to get money without work
People who don't work turn lazy
Work is a duty to society
Work should always come first
Important to meet parent's expectations
Important to meet friend's expectations
Important to meet your own expectations
Important to set your own goals in life
Hard work brings success
There is enough wealth for everyone
Technology benefits the world
Democracy is important
My own country is democratic
My country should solve its own problems first
Justifiable to claim benefits not entitled to
Justifiable to avoid fares
Justifiable to cheat on taxes
Justifiable to take a bribe
People shape their own fate
I see myself as a world citizen
I see myself as a member of the local community
I see myself as a citizen of (country)
I see myself as an autonomous individual
Average correlation
Rev





















Japan
0.05
0.08
0.08
0.05
0.01
0.14
0.08
0.01
0.10
0.15
0.14
0.18
0.04
0.10
0.12
0.09
0.21
0.25
0.11
0.16
0.02
0.13
0.03
0.00
−0.01
−0.05
0.01
0.01
0.04
0.06
0.10
0.16
0.15
0.19
0.17
0.09
USA
0.03
0.12
0.16
0.11
0.14
0.12
0.02
−0.07
−0.01
0.01
−0.02
0.12
0.03
0.09
0.14
0.02
0.27
0.15
0.17
0.14
0.00
0.01
0.04
−0.06
0.00
−0.03
0.06
0.02
0.02
0.04
−0.08
0.26
0.12
0.10
0.15
0.07
China
0.15
0.15
0.14
0.14
0.21
0.08
0.11
0.07
0.14
0.18
0.11
0.21
0.08
0.12
0.24
0.14
0.14
0.27
0.25
0.26
0.01
0.06
0.12
0.21
0.03
0.07
−0.03
−0.06
−0.07
−0.06
0.11
0.09
0.13
0.18
0.26
0.12
India
0.05
0.18
0.10
0.13
0.21
0.12
0.28
−0.09
0.29
0.27
0.31
0.25
0.15
0.19
0.31
0.24
0.36
0.38
0.38
0.35
0.17
−0.07
0.10
0.20
0.30
0.12
−0.09
−0.15
−0.10
−0.07
0.12
0.22
0.15
0.22
0.24
0.17
Notes:
Rev – scale reversed to align with variable description and to have common orientation with other,
non-reversed items. Thus all high scores on these 35 items are analogous to “yea” and low scores to
“nay.” Similarly the Schwartz items are reversed so that a high score on the summed ten-item index is
analogous to “yea” (very much like me) and a low score to “nay” (not at all like me).
26
Table A7 Correlations of Schwartz's ten values index with 35 other values for archetypes A & D in
the pooled data
WVS 2005 Items
Importance of family
Importance of friends
Importance of leisure
Importance of politics
Importance of work
Importance of religion
Happiness
Other people are fair
Satisfaction with life
In control of life
Satisfaction with finances
Need a job to develop your talent
Humiliating to get money without work
People who don't work turn lazy
Work is a duty to society
Work should always come first
Important to meet parent's expectations
Important to meet friend's expectations
Important to meet your own expectations
Important to set your own goals in life
Hard work brings success
There is enough wealth for everyone
Technology benefits the world
Democracy is important
My own country is democratic
My country should solve its own problems first
Justifiable to claim benefits not entitled to
Justifiable to avoid fares
Justifiable to cheat on taxes
Justifiable to take a bribe
People shape their own fate
I see myself as a world citizen
I see myself as a member of the local community
I see myself as a citizen of (country)
I see myself as an autonomous individual
Average correlation
Rev





















Pooled
Data
0.07
0.08
0.01
0.03
0.24
0.40
0.15
−0.10
0.09
0.25
0.12
0.33
0.22
0.21
0.31
0.37
0.46
0.40
0.31
0.28
0.23
−0.02
0.29
−0.04
0.09
0.10
0.08
0.07
0.12
0.12
0.02
0.27
0.23
0.36
0.33
0.18
Notes:
Rev – scale reversed to align with variable description and to have common orientation with other,
non-reversed items. Thus all high scores on these 35 items are analogous to “yea” and low scores to
“nay.” Similarly the Schwartz items are reversed so that a high score on the summed ten-item index is
analogous to “yea” (very much like me) and a low score to “nay” (not at all like me).
27
Table A8 Tests for the presence of archetype specific social desirability bias
A
Country
Japan
USA
China
India
Pooled
Japan
USA
China
India
Pooled
6.6
10.8
10.9
17.4
8.8
B
C
D
E
Archetype means for the non-ethical behavior index
5.9
7.7
6.9
7.9
9.1
6.4
8.9
7.8
10.1
9.6
11.7
11.3
12.8
7.0
12.6
10.9
8.4
F
17.3
Comparisons with archetype A1 (significant effect sizes)
ns2
ns
ns
3
−0.42
ns
−0.79 (M)
(S)
−0.36 (S)
−0.57
ns
(M)
−0.87 (L)
−0.56
−0.68 (M)
ns
ns
(M)
−0.32 (S) 0.45 (S)
0.28 (S)
ns
Notes:
1. A is the archetype which least values any of the Schwartz items. It therefore provides an anchor to
test for social desirability bias in the other archetypes B to F.
2. Difference between pairwise means not significant in a Tukey HSD test with a family-wise error of
0.001. (ns – not significant).
3. Where pairwise means significantly differ we show the effect size (Cohen’s d). (L – large, M –
moderate, S – small).
28
Table A9 Archetype profiles on the ten Schwartz values for Japan, USA, China and India
JAPAN
A
B
C
D
USA
A
B
C
D
CHINA
A
B
C
D
INDIA
A
B
C
D
E
F
Schwartz Valuesa
4
5
6
7
1
2
3
8
9
10
2.82
5.86
3.79
2.50
2.60
5.60
4.18
2.11
1.75
5.32
2.58
1.55
1.33
4.93
3.25
3.73
1.22
4.72
3.30
5.40
1.05
1.62
3.97
2.22
1.58
3.15
5.18
2.05
1.16
1.95
3.56
4.73
1.58
2.06
4.91
1.06
2.49
4.43
5.43
1.36
1.42
5.04
5.29
4.81
1.98
5.27
5.33
5.39
1.64
5.70
3.86
4.84
1.73
5.91
1.52
5.18
2.45
5.96
1.36
5.82
1.95
1.04
1.49
4.92
1.89
1.81
2.68
5.89
2.22
2.03
2.23
5.86
1.47
1.05
4.07
5.24
2.26
2.85
5.53
5.86
1.35
4.33
5.90
5.56
2.16
4.48
5.92
5.44
1.12
5.44
5.22
4.60
1.14
5.76
3.76
4.84
1.47
5.29
5.34
5.38
1.64
4.33
1.18
5.56
1.69
2.10
5.01
5.83
1.62
3.54
1.16
5.70
1.71
1.12
1.42
5.22
1.77
1.57
5.08
5.80
1.82
3.25
5.92
6.00
4.84
5.55
2.50
5.93
5.89
5.90
2.90
2.79
1.41
5.93
5.63
5.85
5.44
4.27
1.56
5.92
5.93
5.96
3.87
3.66
2.19
5.45
1.80
6.00
5.38
1.85
2.31
3.00
1.77
5.99
1.32
5.05
2.39
5.06
5.92
5.94
2.22
4.68
2.73
1.14
1.26
5.93
1.39
2.02
2.24
1.21
5.86
5.99
1.84
5.83
3.29
5.62
5.11
5.97
1.54
5.68
Notes: a1-Universalism, 2-Benevolence, 3-Tradition, 4-Conformity, 5-Security, 6-Power, 7Achievement, 8-Hedonisn, 9-Stimulation, 10-Self-direction.
29
Table A10 Schwartz values means and standard deviations for the archetypal and non-archetypal
cases in Japan, USA, China and India
1
2
3
3.29
5.04
3.80
3.18
3.74
3.80
2.95
4.71
4.18
3.11
3.54
3.61
2.24
4.14
2.72
2.21
2.72
2.78
0.90
0.75
1.19
0.92
0.94
1.09
0.78
0.95
0.96
1.01
0.88
1.06
1.02
1.14
1.26
0.99
1.04
1.23
2.55
4.65
4.83
4.24
4.02
4.06
3.23
5.01
4.88
4.87
4.54
4.55
3.01
4.90
3.75
4.50
4.08
4.16
Archetypal A
Archetypal B
Archetypal C
Archetypal D
Non-archetypal
Overall
CHINA
Archetypal A
Archetypal B
Archetypal C
Archetypal D
Non-archetypal
Overall
0.99
0.97
1.01
1.20
1.07
1.24
0.93
0.86
0.78
0.88
0.86
1.02
1.25
1.21
1.47
1.21
1.25
1.37
2.43
4.45
5.26
5.01
4.53
4.59
3.15
4.73
5.40
5.01
4.72
4.78
2.39
4.88
4.61
4.32
4.14
4.21
Archetypal A
Archetypal B
Archetypal C
Archetypal D
Non-archetypal
Overall
INDIA
Archetypal A
1.11
1.22
0.61
0.84
0.98
1.19
1.19
0.96
0.58
0.89
0.89
1.04
1.24
1.16
1.36
1.41
1.44
1.51
2.76
3.37
2.48
JAPAN
Archetypal A
Archetypal B
Archetypal C
Archetypal D
Non-archetypal
Overall
Archetypal A
Archetypal B
Archetypal C
Archetypal D
Non-archetypal
Overall
USA
Archetypal A
Archetypal B
Archetypal C
Archetypal D
Non-archetypal
Overall
Schwartz Valuesa
4
5
6
7
Mean
2.35 2.45 1.50 2.23
4.17 4.15 1.76 3.03
3.38 3.63 3.21 4.39
3.65 4.53 2.25 2.57
3.26 3.56 2.13 2.99
3.24 3.49 2.04 2.92
Standard Deviation
0.80 0.76 0.60 0.87
1.16 1.18 0.76 1.24
1.16 1.05 1.12 0.94
1.16 0.91 0.96 1.24
0.95 0.96 0.84 1.03
1.15 1.15 0.94 1.18
Mean
2.93 3.57 2.22 2.57
4.87 5.04 1.58 2.46
2.48 2.36 1.91 3.17
4.54 5.02 4.05 4.84
3.70 4.22 2.30 3.34
3.88 4.29 2.42 3.31
Standard Deviation
1.16 1.25 1.00 1.15
1.01 0.93 0.66 1.12
1.24 1.08 0.83 1.34
1.10 0.90 0.98 0.95
1.21 1.10 0.89 1.15
1.37 1.30 1.19 1.38
Mean
2.38 2.66 2.40 2.41
5.11 5.04 3.49 2.75
3.88 4.91 2.16 4.41
4.54 4.90 4.59 4.82
4.04 4.51 3.20 3.79
4.09 4.57 3.21 3.87
Standard Deviation
1.15 1.22 1.04 1.09
0.82 0.82 1.34 1.26
1.59 1.00 0.80 1.19
1.30 1.01 1.04 0.92
1.36 1.04 1.24 1.20
1.49 1.20 1.39 1.38
Mean
2.62 3.06 2.73 3.06
8
9
10
1.99
2.26
3.46
3.84
2.82
2.70
1.78
2.06
3.80
1.46
2.15
2.14
2.89
3.86
4.44
2.35
3.43
3.38
0.72
1.03
1.26
1.01
0.89
1.08
0.65
0.90
1.09
0.61
0.80
0.98
1.07
1.30
0.97
0.95
1.12
1.23
2.77
2.36
2.84
4.55
3.26
3.21
2.21
1.81
4.06
4.01
3.01
2.92
3.19
3.60
5.05
4.87
4.27
4.16
1.22
1.09
1.21
1.00
1.13
1.32
1.06
0.87
1.31
1.21
1.12
1.36
1.20
1.25
0.79
0.95
1.08
1.24
2.29
3.20
2.03
4.56
2.93
3.03
2.04
1.61
1.88
4.04
2.28
2.43
2.50
2.35
4.46
4.84
3.68
3.80
0.97
1.44
0.76
1.12
1.23
1.42
1.01
0.59
0.79
1.35
1.04
1.30
1.24
0.95
1.09
0.89
1.29
1.42
2.73
2.94
3.61
30
Archetypal B
Archetypal C
Archetypal D
Archetypal E
Archetypal F
Non-archetypal
Overall
3.45
5.43
5.45
4.97
4.92
4.55
4.64
5.43
5.48
5.35
3.78
2.97
4.57
4.70
5.38
5.42
5.33
5.23
4.63
4.85
4.88
Archetypal A
Archetypal B
Archetypal C
Archetypal D
Archetypal E
Archetypal F
Non-archetypal
Overall
1.01
1.35
0.74
0.78
0.91
0.90
1.10
1.24
1.35
0.66
0.68
0.87
1.40
1.24
1.11
1.24
1.03
0.82
0.86
0.91
0.76
0.90
1.03
1.20
5.37 4.69 3.10 4.95
5.48 2.62 2.40 5.44
5.23 5.37 4.99 5.21
4.09 4.45 1.94 2.77
4.20 3.02 4.57 4.28
4.62 4.03 3.45 4.48
4.69 4.18 3.56 4.55
Standard Deviation
1.08 1.34 1.25 1.19
0.67 1.18 1.41 0.93
0.86 1.20 1.18 0.84
1.01 0.84 1.06 0.89
1.21 1.27 0.96 1.07
1.18 1.17 0.95 1.32
1.21 1.33 1.41 1.12
1.30 1.46 1.56 1.26
1.72
1.70
4.92
1.82
2.36
2.45
2.84
2.26
4.95
5.37
2.45
4.86
3.88
4.05
5.20
4.96
5.35
2.21
5.17
4.55
4.61
1.35
1.01
0.94
1.16
1.13
1.13
1.33
1.66
1.33
1.19
1.18
0.82
1.29
1.02
1.32
1.52
1.38
0.83
1.06
0.80
1.03
0.84
1.13
1.28
Notes:
a
1-Universalism, 2-Benevolence, 3-Tradition, 4-Conformity, 5-Security, 6-Power, 7-Achievement, 8Hedonisn, 9-Stimulation, 10-Self-direction.
“Archetypal” are cases with an association of 0.5 or more with their respective archetype.
“Non-archetypal” are cases with an association of less than 0.5 with every archetype.
31
Table A11 Distribution of archetypal and non-archetypal cases by country with pooled data
Archetypal cases
Country
Japan
USA
China
India
Total
A (%)
32.0
6.4
7.6
4.0
12.5
B (%)
5.2
15.8
17.4
5.1
10.9
C (%)
0.8
2.1
1.7
14.9
4.9
D (%)
2.1
14.0
19.7
28.3
16.0
E (%)
9.5
9.3
8.6
2.4
7.5
Nonarchetypal
cases (%)
50.4
52.4
45.0
45.3
48.3
Total (%)
100.0
100.0
100.0
100.0
100.0
32
Table A12 Relationships between pooled archetypal groups and demographics plus other variables in WVS 2005
Role
percept Attitude to work
ion
Behavior &
Demographics
Schwartz values
Category
NonOverall archetypal
Rev Mean
Mean
A
4.39
4.23

−
Summary description
Universalism
S
6
Benevolence
6

4.51
4.41
Tradition
6

4.05
4.01
Conformity
6

3.99
3.94
Security
6

3.97
4.08
Power
6

2.77
2.68
Achievement
6

3.74
3.69
Hedonism
6

2.80
2.75
Stimulation
6

3.01
2.83
Self-direction
Index of membership of social organizations
6
8

4.09
3.67
4.02
3.32
2
NA
1.49
45.15
1.50
45.58
Education
8
4.59
4.67
Social class
5

2.62
2.62
Humiliating to get money without work
Work is a duty to society
5
5


3.70
3.85
3.59
3.74
Work should always come first
Need a job to develop your talent
5
5

3.41
3.80
3.21
3.74
People who don't work turn lazy
5
3.90
3.80
I see myself as a citizen of (country)
I see myself as an autonomous individual
4
4


3.48
3.08
3.45
2.99
Gender (high = female)
Age


−
−
−
−
−
−
−
−
−
−
−
−
−
−
B C D
+
+
+
+
+
−
−
−
−
−
+
+
+
+
−
+
+
+
+
+
+
+
+
+
+
+
+
−
+
+
+
+ −
+
−
−
E
+
−
−
−
+
−
+
−
+
+
−
+
−
+ + −
+ + −
+
−
−
+
− − + +
Fvalue
Pairwise
R LPD THSD
ES
0.52
CA
*
2.77
(L)
297.5*
*
3.01 (L)
404.8* 0.58 CA
345.9* 0.55
409.7* 0.58
CE
*
2.74 (L)
CE
*
3.47 (L)
404.8* 0.58
496.1* 0.62
BC
*
2.91 (L)
DB
*
2.83 (L)
375.8* 0.57
453.4* 0.60
CA
*
3.33 (L)
DC
*
3.13 (L)
370.6* 0.56
383.7* 0.57
70.6* 0.28
CB
*
3.08 (L)
CA
CA
*
*
3.06 (L)
1.80 (L)
15.6* 0.14
26.8* 0.18
BC
BD
*
*
0.67 (M)
0.68 (M)
14.9* 0.14
2.6 0.06
ED
*
0.47 (S)
CA
ns
0.26 (S)
36.1* 0.21
39.6* 0.22
CE
CE
*
*
1.03 (L)
1.00 (L)
56.5* 0.26
33.3* 0.20
CA
CE
*
*
1.21 (L)
0.77 (M)
21.3* 0.16
38.8* 0.22
33.1* 0.20
CE
*
0.71 (M)
CA
CA
*
*
0.91 (L)
0.94 (L)
33
Nature (Non)-Ethical
Meeting
of work
stance
expectations
Social priorities
Attitudes to life
I see myself as a world citizen
I see myself as a member of the local
community
Important to meet parent's expectations
Important to meet your own expectations
Important to meet friend's expectations
4

3.01
2.91
−
+ +
19.6* 0.15
CA
*
0.56 (M)
4

3.25
3.20
−
+
CA
*
0.66 (M)
4
4
4
2.98
3.19
2.59
2.94
3.12
2.56
1.18 (L)
0.97 (L)
0.73 (M)
3.25
3.16
*
0.75 (M)
10
10
2.74
2.39
2.49
2.21
23.0* 0.17
16.1* 0.14
17.3* 0.15
CA
Justifiable to claim benefits not entitled to
Justifiable to avoid fares
CB
CB
*
*
0.56 (M)
0.65 (M)
Justifiable to cheat on taxes
10
2.19
2.02
*
0.67 (M)
10
10
10
10
2.05
4.42
6.63
4.04
1.90
4.55
6.59
4.22
21.9* 0.16
24.3* 0.17
13.1* 0.13
12.3* 0.12
10.8* 0.12
CB
Justifiable to take a bribe
My work is mostly cognitive
I have complete independence in my work
My work is mostly creative
+
+
+
+
+
+
+
+
−
*
*
*
4
−
−
−
−
CA
CA
CE
Important to set your own goals in life




0.15
18.1*
63.6* 0.27
31.6* 0.20
41.1* 0.22
CB
EC
EA
ED
*
*
*
*
0.70 (M)
0.55 (M)
0.51 (M)
0.44 (S)
Importance of religion
Importance of work
Importance of family
Importance of friends
Importance of leisure
Importance of politics
Technology benefits the world
Other people are fair
In control of life
Hard work brings success
Happiness
Satisfaction with life
Satisfaction with finances
There is enough wealth for everyone
4
4
4
4
4
4
10
10
10
10
4
10
10
10
2.55
3.36
3.88
3.34
2.95
2.57
7.39
5.53
6.79
7.11
3.11
6.67
5.83
6.34
2.53
3.30
3.88
3.35
2.97
2.54
7.15
5.78
6.79
6.94
3.13
6.72
5.83
6.21
0.29
0.16
0.06
0.07
0.10
0.06
0.22
0.15
0.14
0.17
0.06
0.07
0.06
0.13
CA
CB
CA
CA
EC
BC
CA
BC
BA
CA
DA
BC
DC
BD
*
*
ns
ns
*
ns
*
*
*
*
ns
ns
ns
*
1.67 (L)
0.58 (M)
0.22 (S)
0.30 (S)
0.44 (S)
0.29 (S)
1.07 (L)
0.68 (M)
0.52 (M)
0.78 (M)
0.22 (S)
0.31 (S)
0.22 (S)
0.43 (S)








−
−
−
−
+
−
+ −
+ −
+
+
+
+
−
+
− − + +
−
−
+
+ −
−
−
+
+
−
+
−
74.9*
20.7*
2.6
3.5
8.2*
3.2
39.5*
18.2*
16.0*
25.0*
3.2
3.6
2.6
13.0*
34
Democr
acy
People shape their own fate
10
6.61
6.42
Democracy is important
My own country is democratic
My country should solve its own problems first
10
10
10
8.16
6.57
7.90
8.15
6.51
7.80
+
−
7.1*
14.3*
2.8
4.6
0.09
ED
*
0.34 (S)
0.13
0.06
0.08
BD
CE
BA
*
ns
ns
0.45 (S)
0.21 (S)
0.28 (S)
Notes:
S – measurement scale points; Rev – scale reversed to align with variable description.
A, B, C, D, E – culture archetypal groups with pooled data, see main article Figure 8.
R – multivariate correlation; LPD – Largest Pairwise Difference; THSD – Tukey's Honest Significant Difference test.
ES – Effect Size (L: large; M: moderate; S: small); NA – not applicable; * p < 0.001 Bonferroni corrected; ns – not significant.
(+) archetype mean significantly larger than the grand mean; (−) archetype mean significantly smaller than the grand mean.
In the last column, items within each category are arranged in descending order of effect size, from large to small.
35
Figure A1 Schwartz values model and an example of a culture archetype.
Openness to Change
Self-Transcendence
Conservation
Self-Enhancement
36
Figure A2 Plot of residual sum of squares for Japan.
37