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A Tactile Automated Passive-Finger Stimulator TAPS

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

Jason Westra¹, Nathan Tintle¹, Eleanor Rivera¹

¹University of Illinois Chicago, IL, USA.

Journal of Health Psychology

|April 26, 2026

Summary

This summary is machine-generated.

Latent profile analysis (LPA) shows adequate performance for the Revised Illness Perception Questionnaire (IPQ-R) with sample sizes of 100 or more. However, subgroup differences and sample heterogeneity significantly impact accuracy, requiring careful consideration for reliable results.

Keywords:

cluster analysis illness perceptions latent profile analysis statistical methods

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Area of Science:

Psychometrics
Statistical Modeling
Health Psychology

Background:

Latent profile analysis (LPA) is an emerging statistical technique.
The Revised Illness Perception Questionnaire (IPQ-R) is a common tool for assessing illness perceptions.
Integrating LPA with IPQ-R data requires understanding methodological considerations.

Purpose of the Study:

To provide guidance for researchers using LPA with IPQ-R data.
To evaluate the performance of LPA under various simulated conditions.
To offer recommendations for sample size and heterogeneity when applying LPA to IPQ-R.

Main Methods:

Utilized simulated datasets to explore 640 parameters.
Varied sample size, IPQ-R distribution, covariance, and subscale means.
Simulated 3 distinct latent subgroups using the MClust package in R.

Main Results:

LPA performance is inadequate with sample sizes below 100 (detection <50%).
Sample sizes of 100 or greater may still yield suboptimal results based on subgroup sizes and within-group variance.
Increased differences between subgroups enhance LPA accuracy.

Conclusions:

Researchers should exercise caution with sample sizes under 100 when using LPA for IPQ-R.
Sample heterogeneity and subgroup characteristics are critical factors influencing LPA accuracy.
Careful estimation of sample size based on anticipated heterogeneity is crucial for robust LPA findings with IPQ-R data.