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 Menard's (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 McFadden's (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: • How to obtain and inte... ]
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

[@HarryJayCavite]

Thank you for this video. I have a question, how to generate marginal effects (as what is commonly presented in most journal publications) in spss? Can we generate it directly in spss? Thank you once again.

[@moirarising3363]

Many thanks! Can you do this in STATA?