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.

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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
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article

Latent profile analysis for the IPQ-R: Practical analysis recommendations informed by simulation

Jason Westra, Eleanor Rivera, Nathan Tintle
Journal of Health Psychology
Machine Learning in Healthcare
article

Latent profile analysis for the IPQ-R: Practical analysis recommendations informed by simulation

Jason Westra, Eleanor Rivera, Nathan Tintle
article en

Abstract

⩾ 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.

Journal of Health Psychology
University of Illinois Chicago (US)
Openalex Percentile: Top 7%
Machine Learning in Healthcare
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