How to test for common method bias in CB-SEM?

Hello E. V.,

As there are different approaches to "detection" of common method bias, there are different ways that any (CB) SEM package might be used to check for the apparent presence of CMB.

Here's one very readable link that outlines some of the most common approaches, and outlines the check associated with each:

https://www.sciencedirect.com/science/article/abs/pii/S0261517721000492

A bit more detail appears in this article:

Article Common method bias in applied settings: The dilemma of resea...

Good luck with your work.

Christian Geiser

In addition to the articles recommended by David Morse, you might want to take a look at the literature on multitrait-multimethod (MTMM) analysis (Campbell & Fiske, 1959), specifically confirmatory factor analysis (CFA) of MTMM data.

The MTMM paradigm is discussed in:

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105.

Eid, M. & Diener, E. (2006). Handbook of multimethod measurement in psychology. Washington, DC: American Psychological Association.

For a discussion of various CFA-MTMM models see, for example:

Eid, M., Geiser, C., & Koch, T. (2016). Measuring method effects: From traditional to design-oriented approaches. Current Directions in Psychological Science, 25, 275-280.

Eid, M., Lischetzke, T., & Nussbeck, F. W. (2006). Structural Equation Models for Multitrait-Multimethod Data. In M. Eid & E. Diener (Eds.), Handbook of multimethod measurement in psychology (pp. 283–299). American Psychological Association.

Eid, M., Nussbeck, F., Geiser, C., Cole, D., Gollwitzer, M. & Lischetzke, T. (2008). Structural equation modeling of multitrait-multimethod data: Different models for different types of methods. Psychological Methods, 13, 230-253.

Geiser, C., Eid, M., West, S. G., Lischetzke, T., & Nussbeck, F. W. (2012). A comparison of method effects in two confirmatory factor models for structurally different methods. Structural Equation Modeling, 19(3), 409-436.

Kenny, D. A., & Kashy, D. A. (1992). The analysis of the multitrait-multimethod matrix by confirmatory factor analysis. Psychological Bulletin, 112, 165–172. DOI:10.1037/0033-2909.112.1.165

Koch, T., Eid, M., & Lochner, K. (2018). Multitrait-multimethod analysis: The psychometric foundation of CFA-MTMM models. In P. Irwing, T. Booth, & D. Hughes (Eds.), The Wiley-Blackwell Handbook of Psychometric Testing (pp. 781-846). West Sussex, UK: John Wiley & Sons.

Marsh, H. W., & Grayson, D. A. (1995). Latent variable models of multitrait-multimethod data. In R. H. Hoyle (Ed.), Structural equation modeling. Concepts, issues, and applications (pp. 177–198). Thousand Oaks, CA: Sage.

Widaman, K.-F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9, 1–26. DOI: 10.1177/014662168500900101

Munazza Kanwal

Common Method Bias (CMB) refers to the systematic variance that is attributed to the measurement method rather than the constructs being measured in a study. It can potentially lead to inflated relationships between variables in a structural model. Confirmatory factor analysis (CFA) within the context of Covariance-Based Structural Equation Modeling (CB-SEM) is often used to assess and control for CMB. Here's how you can test for common method bias in CB-SEM:

Collect Data and Develop Measurement Model:Ensure that you have collected data using multiple sources, methods, or time points to minimize method bias. Develop a measurement model that includes latent constructs and their observed indicators (items). These constructs should be theoretically derived and well-established in the literature.

Create a Baseline Model:Build a baseline CB-SEM model that includes your latent constructs, their observed indicators, and the relationships between constructs.

Create a Common Method Factor:Create a common method factor (CMF) that includes indicators from all constructs. This factor represents the method variance shared across all constructs due to the measurement method. Include CMF indicators in your model and allow them to load on the CMF factor.

Assess Model Fit:Estimate the CB-SEM model with the CMF and assess the overall model fit using standard fit indices (e.g., CFI, TLI, RMSEA). Compare the fit of this model with the baseline model. If the fit indices degrade significantly in the model with the CMF, it indicates the presence of common method bias.

Conduct Harman's Single-Factor Test:Run an exploratory factor analysis (EFA) on all items in your study, including both constructs and CMF indicators. Check if a single dominant factor (general factor) emerges that explains the majority of variance. If this factor explains a large portion of the variance, it might indicate common method bias.

Analyze Method Variance:Estimate the variance accounted for by the common method factor. If the CMF explains a substantial amount of variance in the items, it suggests the presence of common method bias.

Apply Method-Only Model:Estimate a model that only includes the common method factor and the observed indicators. This method-only model represents the method variance without the influence of the constructs. Assess the model fit for the method-only model. If the fit is acceptable, it provides evidence for the presence of common method bias.

Consider Alternative Explanations:Consider other explanations for the results. Common method bias might not be the only explanation for the findings. Other factors like content overlap, response bias, and true underlying relationships between constructs should also be considered.

Mitigate Common Method Bias:If evidence of common method bias is found, consider applying methods to mitigate its effects, such as using different measurement methods, collecting data from multiple sources, or employing procedural remedies.

Remember that while these steps provide a general framework for assessing common method bias in CB-SEM, the specific implementation might vary based on your research context, data, and the software you are using for analysis.

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