An updated paradigm for evaluating measurement invariance incorporating common method variance and its assessment

Jan-Benedict E.M. Steenkamp, Alberto Maydeu-Olivares

Journal of the Academy of Marketing Science · 2021 · Volume 49 · Issue 1

Abstract

Measurement invariance is necessary before any substantive cross-national comparisons can be made. The statistical workhorse for conducting measurement invariance analyses is the multigroup confirmatory factor analysis model. This model works well if a few items exhibit clearly differential item functioning, but it is not able to capture, model, and control for measurement bias that affects all items, i.e., this model cannot account for common method variance. The presence of common method variance in cross-national data leads to poorly fitting models which in turn often results in biased, if not incorrect, results. We introduce a procedure to analyze and control for common method variance in one’s data, based on a series of factor analysis models with a random intercept. The modeling framework yields constructs and factor scores free of method effects. We use marker variables to support the validity of the interpretation of the random intercept as method factor. An empirical application dealing with material values in Spain, the UK, and Brazil is provided. We compare results with those obtained for the standard multigroup confirmatory factor analysis model.

Keywords

Common method variance, Measurement invariance, Cross-national research method bias, Random-intercept factor models, Confirmatory factor analysis, Structural equation modeling

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