Determining number of factors during EFA using Maximum Likelihood factor analysis in SPSS

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: https://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: https://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: https://youtu.be/AKE49mHkoKo

Видео Determining number of factors during EFA using Maximum Likelihood factor analysis in SPSS канала Mike Crowson