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date: 03 June 2020

Abstract and Keywords

This chapter presents a broad overview of multiple regression (MR), psychology's data analytic workhorse. MR is a statistical method for investigating the relationship between categorical, quantitative, or both types of independent variables and a single dependent variable. An initial literature review documents the broad use of MR in leading journals in clinical psychology. The chapter then provides an understanding of the fundamentals of simple linear regression models, followed by more complex models involving nonlinear effects and interactions. The chapter presents solutions to several potential problems that arise in MR, including missing data, data collected from groups (multilevel modeling), data collected from individuals over time (growth curve models, generalized estimating equations), and noncontinuous dependent variables including binary outcomes, unordered or ordered categories, and counts (generalized linear model). Throughout, the chapter offers advice for clinical researchers on problems that commonly arise in conducting and interpreting MR analyses.

Keywords: Multiple regression, interaction, curvilinear, logistic regression, generalized linear model, growth model, multilevel model

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