Multiple Approaches to Analyzing Count Data in Studies of Individual Differences: The Propensity for Type 1 Errors, Illustrated with the Case of Absenteeism Prediction

The present study compares eight models for analyzing count data: ordinary least squares (OLS), OLS with a transformed dependent variable, Tobit, Poisson, overdispersed Poisson, negative binomial, ordinal logistic, and ordinal probit regressions. Simulation reveals the extent that each model produces false positives. Results suggest that, despite methodological expectations, OLS regression does not produce more false positives than expected by chance. The Tobit and Poisson models yield too many false positives. The negative binomial models produce fewer than expected false positives.Sturman24_Multiple_Approaches_to_Analyzing_Count_Data.pdf: 1857 downloads, before Aug. 1, 2020