Assessment of fit of the time-varying dynamic partial credit model using the posterior predictive model checking method

Sebastian Castro-Alvarez*, Sandip Sinharay, Laura F Bringmann, Rob R Meijer, Jorge N Tendeiro

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Several new models based on item response theory have recently been suggested to analyse intensive longitudinal data. One of these new models is the time-varying dynamic partial credit model (TV-DPCM; Castro-Alvarez et al., Multivariate Behavioral Research, 2023, 1), which is a combination of the partial credit model and the time-varying autoregressive model. The model allows the study of the psychometric properties of the items and the modelling of nonlinear trends at the latent state level. However, there is a severe lack of tools to assess the fit of the TV-DPCM. In this paper, we propose and develop several test statistics and discrepancy measures based on the posterior predictive model checking (PPMC) method (PPMC; Rubin, The Annals of Statistics, 1984, 12, 1151) to assess the fit of the TV-DPCM. Simulated and empirical data are used to study the performance of and illustrate the effectiveness of the PPMC method.

Original languageEnglish
Pages (from-to)532-552
Number of pages21
JournalBritish Journal of Mathematical and Statistical Psychology
Volume77
Issue number3
Early online date21-Feb-2024
DOIs
Publication statusPublished - Nov-2024

Keywords

  • autoregressive model
  • model misfit
  • partial credit model
  • posterior predictive model checking
  • psychological time series
  • time-varying dynamic partial credit model

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