Latent profile analysis for the IPQ-R: Practical analysis recommendations informed by simulation
⩾ 100 still may not yield ideal performance depending on sample (e.g., subgroup sizes, within-group variance). With more differences between subgroups, LPA is more accurate. However, researchers have little control over mean differences, except indirectly (e.g., diverse sample). Researchers using LPA with IPQ-R data must carefully consider anticipated sample heterogeneity to establish appropriate sample size estimates. Resources provided in this manuscript can support these determinations.
Authors
- Jason Westra (ORCID: https://orcid.org/0000-0002-4754-1395)
- Eleanor Rivera (ORCID: https://orcid.org/0000-0002-3111-0611)
- Nathan Tintle
Institutions
- University of Illinois Chicago (US)
Publication Details
- Journal
- Journal of Health Psychology
- Published
- 2026-04-26
- DOI
- https://doi.org/10.1177/13591053261437920
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00