Multiple approaches to analyzing count data in studies of individual differences: The propensity for type I 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, Pois son, overdispersed Poisson, negative binomial, ordinal logistic, and ordina l probit regressions. Simulation reveals the extent that each model produce s false positives. Results suggest that, despite methodological expectation s, OLS regression does not produce more false positives than expected by ch ance. The Tobit and Poisson models yield too many false positives. The nega tive binomial models produce fewer than expected false positives.