Examining Individual Differences in How Interaction Behaviors Change Over Time: A Dyadic Multinomial Logistic Growth Modeling Approach

Miriam Brinberg, Graham D. Bodie, Denise H. Solomon, Susanne M. Jones, Nilam Ram

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Several theoretical perspectives suggest that dyadic experiences are distinguished by patterns of behavioral change that emerge during interactions. Methods for examining change in behavior over time are well elaborated for the study of change along continuous dimensions. Extensions for charting increases and decreases in individuals’ use of specific, categorically defined behaviors, however, are rarely invoked. Greater accessibility of Bayesian frameworks that facilitate formulation and estimation of the requisite models is opening new possibilities. This article provides a primer on how multinomial logistic growth models can be used to examine between-dyad differences in within-dyad behavioral change over the course of an interaction. We describe and illustrate how these models are implemented in the Bayesian framework using data from support conversations between strangers (N = 118 dyads) to examine (RQ1) how six types of listeners’ and disclosers’ behaviors change as support conversations unfold and (RQ2) how the disclosers’ preconversation distress moderates the change in conversation behaviors. The primer concludes with a series of notes on (a) implications of modeling choices, (b) flexibility in modeling nonlinear change, (c) necessity for theory that specifies how and why change trajectories differ, and (d) how multinomial logistic growth models can help refine current theory about dyadic interaction.

Original languageEnglish (US)
JournalPsychological Methods
DOIs
StateAccepted/In press - 2023

Bibliographical note

Publisher Copyright:
© 2023 American Psychological Association

Keywords

  • categorical data
  • dyadic interaction
  • growth models
  • intensive longitudinal data
  • supportive conversations

PubMed: MeSH publication types

  • Journal Article

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