- Authors:
-
-
Scholderer, Joachim
;
Close
0000-0001-9790-3860
Department of Economics and Business Economics, Aarhus BSS, Aarhus University -
Grunert, Klaus G.
;
Close0000-0001-8482-184X
Department of Management - Department of Management - Bartholin, Department of Management, Aarhus BSS, Aarhus University -
Brunsø, Karen
Close0000-0002-9782-8562
Department of Management - Department of Management - Bartholin, Department of Management, Aarhus BSS, Aarhus University
-
Scholderer, Joachim
;
- DOI:
- 10.1016/S0148-2963(02)00475-7
- Abstract:
- Measurement bias in cross-cultural surveys can seriously threaten the validity of hypothesis tests. Direct comparisons of means depend on the assumption that differences in observed variables reflect differences in the underlying constructs, and not an additive bias that may be caused by cultural differences in the understanding of item wording or response category labels. However, experience suggests that additive bias can be found more often than not. Based on the concept of partial measurement invariance (Byrne, Shavelson and Muthén 1989), the present paper develops a procedure for eliminating additive bias from cross-cultural data. The procedure involves four steps: (1) embed a potentially biased item in a factor-analytic measurement model, (2) test for the existence of additive bias between populations, (3) use the factor-analytic model to estimate the magnitude of the bias, and (4) replace differences between observed item means by unbiased estimates of the difference between "true" population means. Sampling moments are derived for the purpose of hypothesis testing. The procedure is demonstrated in a numerical example. Potential applications include comparisons of means from different cultures, and correction of raw data before use in cross-cultural segmentation studies.
- Type:
- Journal article
- Language:
- English
- Published in:
- Journal of Business Research, 2005, Vol 58, Issue 1, p. 72-78
- Keywords:
- MAPP; Context effects; Additive bias; Bias correction; Cross-cultural research; Structural equation modelling
- Main Research Area:
- Social science
- Publication Status:
- Published
- Review type:
- Peer Review
- Submission year:
- 2005
- Scientific Level:
- Scientific
- ID:
- 2171458114
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