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

Br J Math Stat Psychol. 2024 Feb 21. doi: 10.1111/bmsp.12339. Online ahead of print.ABSTRACTSeveral 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.PMID:38379504 | DOI:10.1111/bmsp.12339
Source: The British Journal of Mathematical and Statistical Psychology - Category: Statistics Authors: Source Type: research