comparative value priorities of chinese and new zealand

MRAT converts absolute value scores into scores that indicate the relative importance
of each value in the value system, i.e., the individual’s value priorities.
Schwartz reports that the empirical basis for viewing differences in MRAT as bias is the
findings of many analyses (50 or so, at least) that related value priorities to other
variables, attitudes, behaviour, background. The associations obtained (mean
differences, correlations) when using scores corrected for MRAT are consistently more
supportive of hypotheses based on theorizing about how values should relate to these
other variables than the associations with raw scores. Indeed, with raw scores
associations sometimes reverse from expectations. Schwartz reports that in no case have
raw score associations made better sense than those corrected for MRAT.
Schwartz provides the following detailed instructions to correct for scale use:
1) For correlation analyses:
A. Compute each individual’s total score on all value items and divide by the total
number of items (56 or 57). He calls this the MRAT (Mean RATing for the particular
individual).
B1. Centre scores of each of the items for an individual around that individual’s
MRAT. Then compute scores for the 10 values by taking the means of the centred
items. Use these centred value scores in correlations.
B2. Alternatively, use the raw scores for the 10 values, but use partial correlation to
correlate them with other variables, partialing out their relations to MRAT (i.e., use
MRAT as a covariate).
The following two alternative methods yield identical results.
1. For group mean comparisons, analysis of variance or of covariance (t- tests,
ANOVA, MANOVA, ANCOVA, MANCOVA):
A. Compute MRAT as in 1) A. above
B1. Centre scores for each item and compute 10 value scores as in 1)B1. Then use these
centred scores in the analyses.
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