Uploaded January 2023 | Updated September 2026, 2 weeks ago
In this video I provide a walk through of an example of repeated measures ANOVA involving a single between subjects factor and a single within subjects factor. I discuss both the multivariate and univariate test results, provide an overview of how to interpret the polynomial contrasts (useful only in those cases where levels of the repeated factor are ordered), and interpretation of simple effects tests and profile plots.
You can download a copy of the data here:
drive.google.com/file/d/1tOOI9slDstGCub05j6tJ_9-aIKoYaS8J/view
You can download a supplemental powerpoint here:
drive.google.com/file/d/1iSU4402r5bzKxd55cFHnsBhl-_KOGqBM/view
Be sure to check out my online sites at:
drmcstats.net
sites.google.com/view/statistics-for-the-real-world/home
In this video I provide a walk through of an example of repeated measures ANOVA involving a single between subjects factor and a single within subjects factor. I discuss both the multivariate and univariate test results, provide an overview of how to interpret the polynomial contrasts (useful only in those cases where levels of the repeated factor are ordered), and interpretation of simple effects tests and profile plots.
You can download a copy of the data here:
drive.google.com/file/d/1tOOI9slDstGCub05j6tJ_9-aIKoYaS8J/view
You can download a supplemental powerpoint here:
drive.google.com/file/d/1iSU4402r5bzKxd55cFHnsBhl-_KOGqBM/view
Be sure to check out my online sites at:
drmcstats.net
sites.google.com/view/statistics-for-the-real-world/home


![A super-easy effect size for evaluating the fit of a binary logistic regression using SPSS
This video provides a short demo of an easy-to-generate effect size measure to assess global model fit for your binary logistic regression. The procedure is described by Tabachnick & Fidell (2013) and Menard (2000). It also a mathematical component of Menards (2011) approach to standardizing regression coefficients using his formula.
IMPORTANT: The approach discussed in this video is one of many possible pseudo-R-square approaches approaches (see Menard, 2000) and tests (e.g., likelihood ratio chi-square test; Hosmer & Lemeshow chi-square test) for evaluating and reporting on model fit. Although this is a very easy index to compute, I strongly encourage its use conjunction with other measures of global model fit (such as those just noted) when evaluating the fit of your model.
[Side note: In terms of alternative pseudo R-squares, my preference is McFaddens (which seems to be slightly preferred by (Menard, 2000), as it is more intuitive as a proportional reduction in error variance (p. 24). I review this measure here: https://youtu.be/vY3L_6myOc8]
Articles citing the procedure described in this video:
Menard, S. (2000). Coefficients of determination for multiple logistic regression analysis. The American Statistician , 54, 17-24.
Menard, S. (2011). Standards for standardizing logistic regression coefficients. Social Forces, 89, 1409-1428. (See above comment on this article)
Tabachnick, B.G., & Fidell, L.S. (2013). Using multivariate statistics (6th ed). Pearson: Upper Saddle River, NJ A super-easy effect size for evaluating the fit of a binary logistic regression using SPSS](https://i.ytimg.com/vi/LA5gWgfpHkY/mqdefault.jpg)







