Beyond the mean: Sequence analysis methods for clustering ordinal EMA data

TL;DR

This paper introduces a sequence analysis approach combined with PCA and K-means clustering to identify latent groups in ordinal EMA data, capturing dynamic patterns beyond simple averages.

Contribution

It proposes a novel method for analyzing longitudinal EMA data by integrating sequence analysis with clustering, outperforming traditional latent class and transition analyses.

Findings

The method effectively identifies meaningful latent groups in simulated and real EMA data.

It reveals distinct stress profile groups that relate to cognitive performance.

The approach captures temporal dynamics better than summary statistics.

Abstract

Ecological momentary assessment (EMA) ratings are widely used in studies of behavioral and psychological phenomena to capture real-time data in subjects' real-world environments. Because the data are collected repeatedly over the study period, they provide rich longitudinal rating profiles for each individual. However, the number of observations per subject is often large, while both sample size and sampling intensity can vary substantially across individuals, which complicates the analysis. In some settings, simplified summaries of individual profiles, such as averages computed across the study period, are used for downstream analyses, including regression-style modeling. Although such summaries can be convenient, they may fail to fully capture dynamic temporal patterns present in the complete longitudinal profiles. To address this, we borrow measures from sequence analysis that capture individual-level patterns over time and then applied principal component analysis (PCA) followed by $K$-means clustering to identify unobserved latent groups of individuals with similar profiles. We test our approach using simulated data from a categorical functional regression model and compare its performance with two commonly used methods for detecting unobserved group structures: latent class analysis (LCA), and latent transition analysis (LTA). Using EMA stress observations from a large sample of U.S. adults (Newman et al., 2024, 2025), we identify distinct latent stress profile groups and show that they improve characterization of the impact on cognitive performance.