Common method variance (CMV) is an ongoing topic of debate and concern in the organizational literature. We present four latent variable confirmatory factor analysis model designs for assessing and controlling for CMV—those for unmeasured latent method constructs, Marker Variables, Measured Cause Variables, as well as a new hybrid design wherein these three types of method latent variables are used concurrently. We then describe a comprehensive analysis strategy that can be used with these four designs and provide a demonstration using the new design, the Hybrid Method Variables Model. In our discussion, we comment on different issues related to implementing these designs and analyses, provide supporting practical guidance, and, finally, advocate for the use of the Hybrid Method Variables Model. Through these means, we hope to promote a more comprehensive and consistent approach to the assessment of CMV in the organizational literature and more extensive use of hybrid models that include multiple types of latent method variables to assess CMV.
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It is preferable to test for CMV using a CFA model, as compared to a full structural equation model with exogenous and endogenous latent variables. This preference is based on the fact that the CFA model is the least restrictive in terms of latent variable relations (all latent variables are related to each other), so there is no risk of path model misspecification compromising CMV tests. Also, there are likely fewer estimation/convergence problems due to the complex method variance measurement model when implemented in a CFA vs. a path model.
With LISREL, latent variable standardization is the default, and the factor loadings and error variances to be used are referred to as LISREL Estimates. With Mplus, the default is to achieve identification by setting a referent factor loading equal to 1.0 and the factor variance is estimated, so this default must be released so that the referent factor loading is estimated and the corresponding factor variance is set equal to 1.0. Assuming this has occurred, the Mplus unstandardized estimates are used as fixed values for the relevant factor loadings and error variances.
As part of the original CFA Marker Technique, Williams et al. (2010) also included a Phase III Sensitivity Analysis based on Lindell and Whitney (2001) to address the degree to which conclusions might be influenced by sampling error (see Williams et al. pp. 500; 503). In our current strategy, Sensitivity Analysis is not included, but can be seen as optional. Based on the results of Williams et al., we note that researchers may consider including it only if their sample sizes are very small and there is a concern that sampling error may be influencing their point estimates and method variance effects might be underestimated.
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Williams, L.J., McGonagle, A.K. Four Research Designs and a Comprehensive Analysis Strategy for Investigating Common Method Variance with Self-Report Measures Using Latent Variables. J Bus Psychol 31, 339–359 (2016). https://doi.org/10.1007/s10869-015-9422-9
- Common method variance
- Unmeasured latent method factor
- Marker Variable
- Measured method variable
- Measured Cause Variable
- Hybrid Method Variables Model