Uploaded June 2022 | Updated September 2026, 2 weeks ago

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In this video, I provide a demonstration of an approach (i.e., Maximum likelihood factor analysis) to aid you in making a decision regarding the number of factors that best account for the intercorrelations among your measured variables during factor analysis. In the video, I demonstrate a process of generating measures of model fit for one-, two-, three-, and four-factor models, including the chi-square goodness of fit test and the RMSEA (root mean square error of approximation).

Some of the discussion points I raise are addressed in:

Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis. Oxford University Press.

Feel free to download a copy of the dataset here: drive.google.com/file/d/1xTOJl63NYRAljyUzKyQ0w1R8VNYgp1Px

During the presentation, I use an Excel file to make certain calculations. You can download a copy of the final file here: drive.google.com/file/d/1QjFr7ogdeawvsatPBoDLoL1xVUo5ItLD

In the video, I reference another presentation on Parallel analysis as a basis for determination of factors. Here is the link: youtu.be/AKE49mHkoKo