Academic Insights: An Application of Multiple-Group Causal Models in Assessing Cross-Cultural Measurement Equivalence

Abstract

In cross-national marketing research, the question whether measurement accuracy, reliability, and validity are achieved across samples traditionally has hampered research efforts. Measurement equivalence across samples has particularly perplexed researchers in their efforts to evaluate responses regarding latent variables. Recently, multiple-group structural equations modeling has been suggested as a reliable method for determining measurement equivalence. To date, however, the use of this approach has remained limited. In this study, the authors apply multiple-group structural equations modeling to assess measurement equivalence in three distinct constructs across U.S. and Korean samples on data derived from cross-cultural advertising research. The authors propose an extension of this method using the measurement error covariance matrices, which will enable researchers to evaluate measurement reliability across samples better and to attempt to disentangle cultural differences in instrument usage from measurement-related differences. The authors evaluate the results and discuss the findings, as well as outline the implications and limitations of this method for further research.