CBA Office Hours on Linear Regression

It is critical for researchers in the behavioral, health, and social sciences to have a full understanding of the linear regression model. Not only is this model important in its own right, but it serves as the foundation for more advanced statistical models, such as the multilevel model, factor analysis, structural equation modeling, generalized linear models, and many other techniques. For those seeking a first exposure to linear regression or simply looking for a refresher, we’ve launched a new series of CBA Office Hours videos that starts with the basics of the simple one-predictor model and proceeds to more advanced topics. So far, we’ve posted four episodes:

We intend to add more videos as time goes on, focusing on such topics as interpretation in the multiple regression model, the difference between hierarchical versus simultaneous regression, how to incorporate categorical predictors, and how to test, probe, and plot interactions. To view all of the videos in this series in sequence, simply click the embedded video or go to our YouTube playlist on Linear Regression. You can also follow us on social media to be updated as new videos are added on this and other topics.

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A reviewer recently asked me to comment on the issue of equivalent models in my structural equation model. What is the difference between alternative models and equivalent models within an SEM?

An equivalent model can be thought of as a re-parameterization of the original model. In other words, it is just a different way of “packaging” the same information in the data and no equivalent model can be distinguished from another based on fit alone. If you were to fit a series of equivalent models to the same sample data you obtain exactly the same chi-square test statistic, RMSEA, CFI, TLI, and any other omnibus measure of fit. It is often best to treat this as a limitation of any given study and to potentially present one or a small number of equivalent model options to the reader so that these too might be considered as plausible representations of the data.

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