Abstract
Disentangling heterogeneity in psychological constructs remains vital for identifying homogeneous subgroups that hold practical utility and theoretical importance. The increased popularity of intensive longitudinal designs enables the use of novel statistical methods for identifying classes that incorporate considerations for temporal dynamics. Despite advances in latent state-trait theories and methods, traditional methods have rarely differentiated between situation-specific and person-specific classes. The current study describes a novel latent state-trait model with a discrete state and a discrete trait using Bayesian estimation. An artificial data example illustrates parameter estimation and inference in a large sample. A real data example follows to demonstrate model interpretation. The proposed model can account simultaneously for heterogeneity in stable traits as well as transitory states, given a continuously measured observed variable across multiple persons and multiple occasions. Theoretically, differentiating between time-varying and time-invariant components of class membership can harness the rich information in longitudinal data to disaggregate situation- and person-specific heterogeneity. Methodologically, our model complements existing methods for identifying mixture structure in latent states and traits.
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Acknowledgements
Qimin Liu is grateful for the support from the Society of Multivariate Experimental Psychology Dissertation Research Grant.
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Conceptualization: Q. Liu. Methodology: Q. Liu. Formal analysis: Q. Liu. Investigation: Q. Liu. Writing—original draft preparation: Q. Liu. Writing—review and editing: Q. Liu and D.A. Cole. Supervision: D.A. Cole. Project administration: Q. Liu
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Data, Materials, Code, and Online Resources. Simulation code for the artificial data example is available in Appendix 1. Program code for the proposed model is available in Appendix 2. The study is not preregistered. All materials (JAGS code, Artificial Data Example Code, Real Data Example Code, Real Data) are available at https://osf.io/5nkfy/?view_only=99b92ee1cf1645a3968cd062a3455edc. (DOCX 16 kb)
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Liu, Q., Cole, D.A. Disaggregating Person- and Situation-Specific Heterogeneity: a Categorical Latent State-Trait Model. Comput Brain Behav 7, 150–162 (2024). https://doi.org/10.1007/s42113-023-00181-6
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DOI: https://doi.org/10.1007/s42113-023-00181-6