Abstract
In this chapter, we discuss structural equation models in R. We show how R can be used for both covariance-based and partial least squares modeling, and present basic guidelines for model assessment. We also demonstrate the power of R to simulate data and use such simulation to inform our expectations.
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Notes
- 1.
If you are experienced with other SEM software, you may wonder about details such as the need to fix a path for each factor and to specify error terms. Those are automatically handled by lavaan with defaults that are appropriate for many situations (for instance, having uncorrelated errors and fixing the first manifest variable path to 1.0).
- 2.
Always be wary of models that assert or test independence; a well-known phenomenon in human research is that within a given domain, “everything correlates with everything else.” Paul Meehl referred to this as the “crud” factor in research, and showed that it leads to research that finds “significant” associations everywhere [137].
- 3.
In general, fixing parameters is not recommended; the whole point of SEM is to estimate parameters. However, in some cases, especially with smaller samples as we consider here, it may help to focus a model on the influences under consideration if one constrains factors. It is possible with lavaan to constrain to any value, not just 0.
- 4.
The small sample exacerbates another reason for estimation difficulty: our data is highly collinear due to the factor structure imposed when we simulated it to match the report by Iacobucci [104].
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Chapman, C., Feit, E.M. (2019). Confirmatory Factor Analysis and Structural Equation Modeling. In: R For Marketing Research and Analytics. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-030-14316-9_10
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DOI: https://doi.org/10.1007/978-3-030-14316-9_10
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