Flexible Treatment of Time-Varying Covariates with Time Unstructured Data

Daniel McNeish, Tyler H. Matta

Research output: Contribution to journalArticle

Abstract

Time-varying covariates (TVCs) are a common component of growth models. Though mixed effect models (MEMs) and latent curve models (LCMs) are often seen as interchangeable, LCMs are generally more flexible for accommodating TVCs. Specifically, the standard MEM constrains the effect of TVCs across time-points whereas the typical LCM specification can estimate time-specific TVC effects, can include lagged TVC effects, or constrain some TVC effects based on theoretically appropriate phases. However, when data are time-unstructured, LCMs can have difficulty providing TVC effects whose interpretation aligns with typical research questions. This paper shows how MEMs can be adapted to yield TVC effects that mirror the flexibility of LCMs such that the model likelihoods are identical in ideal circumstances. We then extend this adaptation to the context of time-unstructured data where MEMs tend to be more flexible than LCMs. Examples and software code are provided to facilitate implementation of these methods.

Original languageEnglish (US)
Pages (from-to)298-317
Number of pages20
JournalStructural Equation Modeling
Volume27
Issue number2
DOIs
StatePublished - Mar 3 2020

Keywords

  • Growth model
  • multilevel model
  • random effects model
  • time-unstructured data
  • time-varying covariate
  • unbalanced data

ASJC Scopus subject areas

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

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