Ordinal state-trait regression for intensive longitudinal data

Prince P. Osei, Philip T. Reiss

Research output: Contribution to journalArticlepeer-review

Abstract

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalBritish Journal of Mathematical and Statistical Psychology
Volume76
Issue number1
DOIs
StatePublished - Feb 2023

Bibliographical note

Publisher Copyright:
© 2022 The Authors. British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

Keywords

  • cumulative logistic regression
  • experience sampling
  • latent response
  • ordinal mixed-effects model
  • quadratic penalty

ASJC Scopus subject areas

  • General Psychology
  • Arts and Humanities (miscellaneous)
  • Statistics and Probability

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