Tutorial on Using Regression Models with Count Outcomes Using R.

Abstract

Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares) either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix. Count variables such as number of times a student reached a goal, discipline referrals, and absences are ubiquitous in school settings. After a review of published single-case design studies Shadish and Sullivan (2011) recently concluded that nearly all outcome variables were some form of a count. Yet, most analyses they reviewed used traditional data analysis methods designed for normally-distributed continuous data.