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

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.