Model fit criteria curve behaviour in class enumeration – a diagnostic tool for model (mis)specification in longitudinal mixture modelling. Issue 8 (24th May 2022)

• Record Type: Journal Article
• Authors: van der Nest, Gavin; Lima Passos, Valeria; Candel, Math J. J. M.; van Breukelen, Gerard J. P.
• Is Part Of: Journal of statistical computation and simulation. Volume 92:Issue 8(2022)
• Page Start: 1640
• Page End: 1672
• Publication Date: 2022-05-24
• Subjects: Growth mixture model -- latent class growth analysis -- trajectory -- repeated measures -- covariance misspecification -- class extraction; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2021.2004141
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : The use of longitudinal finite mixture models (FMMs) to identify latent classes of individuals following similar paths of temporal development is gaining traction in applied research. However, FMM's users may be unaware of how data features as well as the inappropriate specification of the model's covariance structure impacts class enumeration. To elucidate this, we investigated model fit-criteria curve behaviour across an array of data conditions and covariance structures. Fit statistic patterns were variable among the fit criteria and across a range of data conditions. This variability was greatly attributable to the level of class separation and the presence/absence of random effects. Our findings support some widely held notions (e.g. BIC outperforms other criteria) while debunking others (adding random effects is not always the solution). Based on the obtained results, we present guidelines on how the behaviour of fit criteria curves can be used as a diagnostic aid during class enumeration.