Common Method Variance: Statistical Detection and Control

Abstract

Common Method Variance (CMV) may arise from monomethod measurement of both exogenous and endogenous variables. It has caused concerns about its effects on causal inferences. One way to partition out CMV is specifying a method factor measured by indicators of both the exogenous and endogenous variables in a model. This method is known as Common Method Factor Modeling (CMFM). However, it remained unclear what to expect when CMFM is applied to data with unknown magnitude of CMV, whether the method factor loadings should be estimated freely or with equality constraints, and whether model comparison was viable for detecting CMV. The results of three simulation studies have demonstrated the limitations of detecting CMV with model comparisons, the advantages of power analysis applied to empirical data and Bayesian estimation of CMFM. Specifically, model comparisons usually fail the estimation with misspecified CMFM in small samples; Power analyses through simulating several sample sizes could better determine whether CMFM has been misspecified or CMV may not be detected due to lack of power. Bayesian estimation of CMFM has hardly any convergence problem and best priors may be determined by comparing Deviance Information Criterion (DIC).