Login to use UniMult now!

Login or Create a new Account
Forgot Login?   Sign up  

Paper presented at the 2014 annual meeting of the American Psychological Association, Washington, DC.
Associated file: APA Poster TablesFINAL.doc.


Approximations for Chi-square and F distributions can both be computed for least squares data analysis to provide a p  to evaluate Type I error. Traditionally Chi-square has been used for cross-tab tables and F ratios for regression and ANOVA but either statistic can be applied in both situations. We examined when one statistic may be more accurate than the other for Type I error rates across types of analysis (cross-tabs, regression, ANOVA) and Ns using 25,000 replications per condition. F ratios were closer to nominal Type I error rates in general than Chi-squares. F ratios were more consistent for contingency table count data across variations in Type I levels than Chi-squares. The more the N was less that 100, the more Chi square under-estimated the p. There was no evidence of need for special treatment of dichotomous dependent variables. The most accurate p's are always with F ratios. It seems Chi square should no longer be used or taught.

Requests for reprints should be sent to This email address is being protected from spambots. You need JavaScript enabled to view it..

View Document

View Tables