Using Multilevel Random Coefficient Modeling to Analyze Social Interaction Diary Data

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Abstract
This article provides a rationale for using multilevel random coefficient modeling (MRCM) to analyze social interaction diary data, and describes how to conduct such analyses. This description includes how to examine relationships between individual differences in social interaction and other trait-level individual differences such as personality scales. Analyzing relationships among different ratings of interactions and analyzing differences among types of interactions are also described, including techniques to examine how relationships between trait-level individual differences and reactions to interaction vary across different types of interactions (e.g., interactions with different relational partners). Different types of analyses are illustrated using data from actual studies. Although this article focuses on the analysis of data collected using variants of the Rochester Interaction Record, the techniques described can be used to analyze data collected using other methods and protocols.