The Social Consequences and Mechanisms of Personality: How to Analyse Longitudinal Data from Individual, Dyadic, Round–Robin and Network Designs

Abstract

There is a growing interest among personality psychologists in the processes underlying the social consequences of personality. To adequately tackle this issue, complex designs and sophisticated mathematical models must be employed. In this article, we describe established and novel statistical approaches to examine social consequences of personality for individual, dyadic and group (round–robin and network) data. Our overview includes response surface analysis (RSA), autoregressive path models and latent growth curve models for individual data; actor–partner interdependence models and dyadic RSAs for dyadic data; and social relations and social network analysis for round–robin and network data. Altogether, our goal is to provide an overview of various analytical approaches, the situations in which each can be employed and a first impression about how to interpret their results. Three demo data sets and scripts show how to implement the approaches in R. Copyright © 2015 European Association of Personality Psychology

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 98 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.