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


Abstract


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.

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