Generalized Linear Interactive Modeling: Four Wrongs Don’t Make a Right

Paul R. Yarnold

Optimal Data Analysis, LLC

Several examples used to illustrate generalized linear interactive modeling violate crucial assumptions underlying chi-square, advocate arbitrary parsing of attributes, and conduct statistically unmotivated agglomeration of class categories. Violating assumptions call the validity of the estimated effect and associated Type I error rate into question, and arbitrary parsing and agglomerating procedures can reduce model classification accuracy at best, and mask effects that exist or identify paradoxical effects at worst.

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