Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models.

TLDR

Marital and family researchers often study infrequent behaviors such as abuse, criticism, and drug use, which have important ramifications for families and society, yet most continue to rely on ordinary least‑squares regression that can produce seriously biased estimates for such count data. This article presents a tutorial on statistical methods for positively skewed event data, including Poisson, negative binomial, zero‑inflated Poisson, and zero‑inflated negative binomial regression models. These statistical methods are introduced through a marital commitment example, and the data and computer code to run the example analyses in R, SAS, SPSS, and Mplus are included in the online supplemental material. Extensions and practical advice are given to assist researchers in using these tools with their data.

Abstract

Marital and family researchers often study infrequent behaviors. These powerful psychological variables, such as abuse, criticism, and drug use, have important ramifications for families and society as well as for the statistical models used to study them. Most researchers continue to rely on ordinary least-squares (OLS) regression for these types of data, but estimates and inferences from OLS regression can be seriously biased for count data such as these. This article presents a tutorial on statistical methods for positively skewed event data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. These statistical methods are introduced through a marital commitment example, and the data and computer code to run the example analyses in R, SAS, SPSS, and Mplus are included in the online supplemental material. Extensions and practical advice are given to assist researchers in using these tools with their data.