Uploaded June 2026 | Updated September 2026, 2 weeks ago
This video focuses on descriptive statistics in relation to nominal and ordinal scaled variables.
Powerpoint from video: drive.google.com/file/d/1lps1taKOfhxer49jUJmqJl6xln66_o-J/view
Part 2 (video): youtu.be/aP3SIl5FI7s (Video focuses on descriptive statistics in relation to interval and ratio scaled variables)
Part 3 (video): youtu.be/3ytaZguAuI0 (Video demonstrates analyses using SPSS)
This video focuses on descriptive statistics in relation to nominal and ordinal scaled variables.
Powerpoint from video: drive.google.com/file/d/1lps1taKOfhxer49jUJmqJl6xln66_o-J/view
Part 2 (video): youtu.be/aP3SIl5FI7s (Video focuses on descriptive statistics in relation to interval and ratio scaled variables)
Part 3 (video): youtu.be/3ytaZguAuI0 (Video demonstrates analyses using SPSS)







![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)


