Fixed effects regression in SPSS 28 for repeated measures/longitudinal data (video 2 of 3)

This video is the second in a series on fixed effects (panel) regression in SPSS for repeated measures/longitudinal data (the first one found at https://youtu.be/cN_4RoeKlT4). The first video demonstrated how to predict a time-varying outcome variables as a function of time-varying predictors. In this second video, I show you how it is possible to reparameterize a one-way ANOVA in the form of a fixed-effects (panel) regression in SPSS. I show you this only to offer further clarification on what is occurring in the context of a fixed effects (panel) regression. In this video I use the Least squares dummy variable approach. Download a copy of the data in wide format here: https://drive.google.com/file/d/14DipMMNEKZbPfPSUeYpHci4jfHgf6e7T/view?usp=sharing Download a copy of the data after restructuring and dummy coding here: https://drive.google.com/file/d/1jsMJk0esLpHJEJ7KXR8Ays0WqjgUvZkj/view?usp=sharing Download supplemental powerpoint here: https://drive.google.com/file/d/1cCpz9ZlpzxYC77Rgwb10qi_9Cl3iI_oT/ Important! Ordinarily I would not suggest this reparameterization since the fixed effects approach is more cumbersome (e.g., requiring dummy variables) than the one-way repeated measures ANOVA option in SPSS. Also, there are no degrees of freedom adjustments (using the approach shown here) for those cases where there is evidence of a violation of compound symmetry or sphericity. When performing a one-way repeated measures ANOVA through the usual route in SPSS, more conservative tests (when a violation of CS or sphericity may have occurred) are provided. My first video on fixed effects regression for repeated measures/longitudinal data can be found here: https://youtu.be/https://youtu.be/cN_4RoeKlT4 A third video on fixed effects regression for repeated measures/longitudinal data can be found here: https://youtu.be/Owy_RhA7iL4 Video on dummy coding in SPSS: https://youtu.be/PVYCpeRMvp8