Measurement in Intensive Longitudinal Data

Daniel McNeish, David P. Mackinnon, Lisa A. Marsch, Russell A. Poldrack

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Technological advances have increased the prevalence of intensive longitudinal data as well as statistical techniques appropriate for these data, such as dynamic structural equation modeling (DSEM). Intensive longitudinal designs often investigate constructs related to affect or mood and do so with multiple item scales. However, applications of intensive longitudinal methods often rely on simple sums or averages of the administered items rather than considering a proper measurement model. This paper demonstrates how to incorporate measurement models into DSEM to (1) provide more rigorous measurement of constructs used in intensive longitudinal studies and (2) assess whether scales are invariant across time and across people, which is not possible when item responses are summed or averaged. We provide an example from an ecological momentary assessment study on self-regulation in adults with binge eating disorder and walkthrough how to fit the model in Mplus and how to interpret the results.

Original languageEnglish (US)
Pages (from-to)807-822
Number of pages16
JournalStructural Equation Modeling
Issue number5
StatePublished - 2021


  • EMA
  • Measurement invariance
  • cross-classified factor analysis
  • dynamic structural equation modelling
  • time-series analysis

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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