inequivalent if they do not share any relevant characteristic. Comparisons then lack an attribute for comparison,
also called tertium comparationis (the third term in the comparison). Such views can be found among authors who
argue that human experiences are made up by and therefore bound to their cultural context (emic perspective).
Proponents of an emic view according to whom psychological phenomena are fundamentally incomparable
across cultures would argue in favor of construct inequivalence of psychological constructs.
The second type is called structural ( orfunctional) equivalence . An instrument administered in different cul-
tural groups shows structural equivalence if it measures the same construct in each group (e.g., Raven’s
Standard Progressive Matrices test has been found to measure intelligence in various cultural groups;
Brouwers, Van de Vijver, & Van Hemert, 2009 ). Exploratory factor analyses (EFA) followed by target rotations
or confirmatory factor analysis (CFA) of covariance matrices (in structural equation modeling) can examine
structural equivalence. Structural equivalence does not presuppose identity of measures across groups. If mea-
sures use different stimuli across groups, structural equivalence can be examined by comparing nomological
networks across groups.
The third type of equivalence is called measurement unit ormetric equivalence . Instruments show this type of
equivalence if their measurement scales have the same units of measurement and a different origin (such as the
Celsius and Kelvin temperature scales). This type of equivalence assumes interval- or ratio-level scores with
the same measurement units in each culture. It applies when a bias factor with a fairly uniform influence on the
items of an instrument affects test scores of different cultural groups in a differential way. Social desirability and
stimulus familiarity may exert this influence. Observed group differences in scores are then a mixture of valid
cross-cultural differences and measurement artifacts. When the relative contribution of both sources cannot be
estimated, the interpretation of group comparisons of mean scores remains ambiguous. Multigroup comparisons
of CFA models have been utilized to examine measurement unit equivalence ( Byrne, 2012 ). This technique, based
on comparisons of covariance matrices, can identify bias sources that affect the covariance of items or tests but it
cannot differentiate between valid differences in mean scores and bias sources with a fairly uniform influence on
all parts of an instrument (examples of such influences would be response styles like acquiescence or extremity
scoring).
Only in the case of the fourth type, scalar ( orfull-score) equivalence , can direct comparisons be made; this is the
only type of equivalence that allows the conclusion that average scores obtained in two cultures are different (or
not). This type of equivalence assumes the same interval or ratio scales with the same origin across groups.
Conclusions about which of the latter two types of equivalence applies are often subject to interpretation differ-
ences and controversy. For example, racial differences in intelligence test scores have been interpreted as due to
valid differences (scalar equivalence) and as reflecting measurement artifacts such as stimulus familiarity or
social desirability (measurement unit equivalence). Scalar equivalence assumes that the role of bias can be
neglected. The demonstration of scalar equivalence draws on inductive argumentation, and it is easier to dis-
prove than to prove scalar equivalence. An argument supporting scalar equivalence can be made plausible by
measuring presumably relevant sources of bias (such as stimulus familiarity or social desirability) and showing
that they cannot statistically explain observed cross-cultural differences in a multiple regression or covariance
analysis.
Structural, measurement unit, and scalar equivalence are hierarchically ordered. While higher levels of equiva-
lence are more difficult to establish, it is easier to demonstrate that an instrument measures the same construct in
different cultural groups (structural equivalence) than to demonstrate numerical comparability across cultures
(scalar equivalence). Yet, higher levels of equivalence allow for more detailed comparisons of scores (and hence,
constructs) across cultures. Whereas with structural equivalence, only factor structures and nomological networks
can be compared, scalar equivalence allows for more fine-grained analyses of cross-cultural similarities and
differences.
It has become increasingly popular to apply structural equation modeling (SEM) to investigate equivalence in
so-called multigroup comparisons ( Byrne, 2012 ) used to test different levels of equivalence, ranging from struc-
tural to scalar equivalence. The former is supported if factor loadings are identical across cultures. The latter is
usually operationalized as identical intercepts in regression lines that link items to their construct. Multigroup
options are available in SEM software packages, are rigorous, have a firm statistical basis, and allow for a
detailed test between different levels of equivalence, although it is easy to overlook sources of bias. For example,
if an instrument to assess extraversion is administered in different cultures, a multigroup comparison may pro-
vide a detailed view on sources of incomparability in items, but not reveal that important indicators of extraver-
sion (such as specific manifestations of extraversion in that culture) may have been omitted. While an item about
‘being the life of the party’ may perform poorly in a particular culture, such an analysis will not suggest755 26. MEASURES OF PERSONALITY ACROSS CULTURES
VI. SOCIOCULTURAL INTERACTION AND CONFLICT