The Use of Intensive Longitudinal Methods in Explanatory Personality Research

Haran Sened*, Gal Lazarus, Marci E.J. Gleason, Eshkol Rafaeli, William Fleeson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Intensive longitudinal methods (ILMs), in which data are gathered from participants multiple times with short intervals (typically 24 hours or less apart), have gained considerable ground in personality research and may be useful in exploring causality in both classic personality trait models and more novel contextualized personality state models. We briefly review the various terms and uses of ILMs in various fields of psychology and present five main strategies that can help researchers infer causality in ILM studies. We discuss the use of temporal precedence to establish causality, through both lagged analyses and natural experiments; the use of external measures and peer reports to go beyond self-report data; delving deeper into repeated measures to derive new indices; the use of contextual factors occurring during the measurement period; and combining experimental methods and ILMs. These strategies are illustrated by examples from existing research and by new empirical findings from two dyadic daily diary studies (N = 80 and N = 108 couples) and an experience sampling method study of personality states (N = 52). We conclude by offering a short checklist for designing ILM studies with causality in mind and look at the applicability of these strategies in the intersection of personality psychology and other psychological research domains.

Original languageAmerican English
Pages (from-to)269-285
Number of pages17
JournalEuropean Journal of Personality
Volume32
Issue number3
DOIs
StatePublished - 1 May 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
Copyright © 2018 European Association of Personality Psychology

Keywords

  • causality
  • empirical methods
  • experience sampling methods
  • intensive longitudinal methods

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