Increasingly, social and personality psychologists are conducting studies in which data are collected simultaneously at multiple levels, with hypotheses concerning effects that involve multiple levels of analysis. In studies of naturally occurring social interaction, data describing people and their social interactions are collected simultaneously. This article discuses how to analyze such data using random coefficient modeling. Analyzing data describing day-to-day social interaction is used to illustrate the analysis of event-contingent data (when specific events trigger or organize data collection), and analyzing data describing reactions to daily events is used to illustrate the analysis of interval-contingent data (when data are collected at intervals). Different analytic strategies are presented, the shortcomings of ordinary least squares analyses are described, and the use of multilevel random coefficient modeling is discussed in detail. Different modeling techniques, the specifics of formulating and testing hypotheses, and the differences between fixed and random effects are also considered.

Two studies found positive relationships between the ability to manage emotions and the quality of social interactions, supporting the predictive and incremental validity of an ability measure of emotional intelligence, the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). In a sample of 118 American college students (Study 1), higher scores on the managing emotions subscale of the MSCEIT were positively related to the quality of interactions with friends, evaluated separately by participants and two friends. In a diary study of social interaction with 103 German college students (Study 2), managing emotions scores were positively related to the perceived quality of interactions with opposite sex individuals. Scores on this subscale were also positively related to perceived success in impression management in social interactions with individuals of the opposite sex. In both studies, the main findings remained statistically significant after controlling for Big Five personality traits.

How people's feelings change across time can be represented as trajectories in a core affect space defined by the dimensions of valence and activation. In this article, the authors analyzed individual differences in within-person affective variability defined as characteristics of core affect trajectories, introducing new ways to conceptualize affective variability. In 2 studies, participants provided multiple reports across time describing how they were feeling in terms of core affect. From these data, characteristics of participants' core affect trajectories were derived. Across both studies, core affect variability was negatively related to average valence, self-esteem, and agreeableness, and it was positively related to neuroticism and depression. Moreover, spin, a measure of how much people experienced qualitatively different feelings within the core affect space, was related more consistently to trait measures of adjustment and personality than other measures of within-person variability, including widely used measures of within-person single-dimension standard deviations.