[Feature Request]: Latent Class Growth Analysis and Growth Mixture Modelling

[TarandeepKang]

Description

No response

Purpose

Understanding heterogeneity in individual outcomes over time

Use-case

See description in additional context

Is your feature request related to a problem?

No response

Is your feature request related to a JASP module?

No response

Describe the solution you would like

Implement growth mixture modelling as an option in JASP

Describe alternatives that you have considered

Use R/ Mplus, the second of which is proprietary and expensive

Additional context

Klaas Wardenaar,- in his excellent tutorial,- introduces these approaches saying that they are “used to explain between subject heterogeneity in growth on an outcome by identifying latent classes with different growth trajectories.”

PsyArXiv Preprints | Latent Class Growth Analysis and Growth Mixture Modeling using R: A tutorial for two R-packages and a comparison with Mplus.

This request is related to #1505, but is an extension of the latent class analysis suggested there.

My preference for an R package to use when conducting this analysis is lcmm:

Proust-Lima, C., Philipps, V., & Liquet, B. (2017). Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm. Journal of Statistical Software, 78(2). https://doi.org/10.18637/jss.v078.i02

An example, albeit using Mplus, of this type of analysis from my field of research can be seen in:

Tao, T. J., Liang, L., Liu, H., Hobfoll, S. E., Hou, W. K., & Bonanno, G. A. (2023). The interrelations between psychological outcome trajectories and resource changes amid large-scale disasters: A growth mixture modeling analysis. Translational Psychiatry, 13(1), Article 1. https://doi.org/10.1038/s41398-023-02350-4

This paper provides an overview of the different use cases for the various trajectory modelling approaches, including latent transition analysis which I forgot to mention above. It also discusses various software implementations more comprehensively. But I would say that you should be able to cover the complete set of tools with two or three packages:

Nguena Nguefack, H. L., Pagé, M. G., Katz, J., Choinière, M., Vanasse, A., Dorais, M., Samb, O. M., & Lacasse, A. (2020). Trajectory Modelling Techniques Useful to Epidemiological Research: A Comparative Narrative Review of Approaches. Clinical Epidemiology, 12, 1205–1222. https://doi.org/10.2147/CLEP.S265287

[tomtomme]

@TarandeepKang Would this fit better into the SEM or the timeseries module?

[TarandeepKang]

I'd say either SEM as you've assigned or some yet-to-be-created "mixture", module, perhaps alongside #1505