A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data

David C. Atkins

doi:10.1037/a0029508

A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data

2013-01-01

379

OA

AI

DOI：10.1037/a0029508

原文求助

摘要

En 中文

Critical research questions in the study of addictive behaviors concern how these behaviors change over time: either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website.

Keyword:

count regression

longitudinal data

multilevel models

A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data

摘要

期刊

机构

引用论文