Uploaded February 2023 | Updated September 2026, 2 weeks ago
This video demonstrates how to convert numeric into factor variables. I begin by creating a small data frame in R Studio. From there, I walk through steps for converting numeric variables into factor variables, and also how to add value labels that will show up in your output.
Link to R script file from video here:
drive.google.com/file/d/1vqbn8bhDSdwUVXcSP2aZ7ieS7Speu-0z/view
Additional link:
drive.google.com/file/d/1oP1MA2SjQXKHgtERvjZMsldgDLPlTgXY/view
Be sure to check out my online site at:
sites.google.com/view/statistics-for-the-real-world/home
This video demonstrates how to convert numeric into factor variables. I begin by creating a small data frame in R Studio. From there, I walk through steps for converting numeric variables into factor variables, and also how to add value labels that will show up in your output.
Link to R script file from video here:
drive.google.com/file/d/1vqbn8bhDSdwUVXcSP2aZ7ieS7Speu-0z/view
Additional link:
drive.google.com/file/d/1oP1MA2SjQXKHgtERvjZMsldgDLPlTgXY/view
Be sure to check out my online site at:
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)






