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
JournalBritish Journal of Mathematical and Statistical Psychology
DOIs
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
The authors thank Roy Baumeister, Wilhelm Hofmann, Kathleen Vohs and Amy Summerville for helpful discussions regarding the data, Meng Xu for contributions to software development, and associate editor Eva Ceulemans and the referees for valuable feedback. This work was supported by Israel Science Foundation grant 1076/19.

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

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

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