Capturing the interplay among within- and between-person processes using multilevel modeling techniques

Abstract

Multilevel modeling is an excellent way to analyze nested or clustered data of the type commonly collected through investigations into the linkages between psychological functioning and relationship processes. This article describes two especially relevant applications of multilevel modeling. The first application, growth curve analysis, is already familiar to many researchers and involves modeling individuals’ change trajectories over time and relating the derived change parameters to person-level characteristics or phenomena. The purpose of this paper is to emphasize a second application, multilevel process analysis, which involves modeling within-subject characteristics other than change over a representation of time. Multilevel analysis of within-subject processes is particularly well-suited for hypotheses common to clinical psychology investigations, yet has received substantially less attention in the literature than its growth curve counterpart. Types of research questions and methodologies that can be addressed within the multilevel process analysis framework are described. Finally, aspects of multilevel process analysis are demonstrated with daily diary data collected from wives who reported on their marital happiness and depressed mood for 3 weeks.