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The impact of predictor variable(s) with skewed cell probabilities on the Wald test in binary logistic regression

University of British Columbia

What happens to the parameter estimates and test operating characteristics when the predictor variables in a logistic regression are skewed? The statistics literature provides relatively few answers to this question. A series of simulation studies are reported that investigated the impact of a skewed predictor (s) on the Type I error rate and power of the Wald test in a logistic regression model. Five simulations were conducted for three different models: a simple logistic regression with a binary predictor, a simple logistic regression with a continuous predictor, and a multiple logistic regression with two dichotomous predictors. The results show that the Type I error rate and power were affected by severe predictor skewness, but that the effect was moderated by sample size. The Type I error rate was consistently deflated for all three models. Also, power improved with less skewness. A detailed description of the impact of skewed cell predictor probabilities and sample size provide guidelines for practitioners as to where to expect the greatest problems. These findings highlight the importance of the effects of predictor characteristics on statistical analysis of a logistic regression.

Text

2017-04-13

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/61232

Measurement, Evaluation and Research Methodology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/