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Detecting association changes in intensive longitudinal data in real time: An exponentially weighted moving average
Evelien Schat1, Sarah Schrevens1, Francis Tuerlinckx1
1Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium.
The British Journal of Mathematical and Statistical Psychology
|February 24, 2026
Summary
Real-time detection of worsening well-being is possible using the exponentially weighted moving average (EWMA) procedure. Monitoring Pearson or Spearman correlation is recommended when only association changes are expected.
Area of Science:
- Psychological assessment
- Data analysis
- Time series analysis
Background:
- Within-person changes in associations can signal declining well-being and functioning.
- Real-time detection methods are needed to identify these negative shifts promptly.
Purpose of the Study:
- To evaluate the effectiveness of the exponentially weighted moving average (EWMA) procedure for real-time detection of changes in linear associations.
- To assess the influence of mean and variance changes on EWMA detection performance.
Main Methods:
- Calculating association strength within time windows using various measures (Pearson correlation, Spearman correlation, covariance, distances).
- Monitoring mean-level changes in these association scores using EWMA.
- Simulating scenarios with changes in mean, variance, and correlation to test detection performance.
Main Results:
- Monitoring Pearson and Spearman correlation scores with EWMA is advised when only association changes are anticipated.
- Alternative association measures, sensitive to mean and variance shifts, can enhance detection under specific combined change scenarios.
- Detection performance is influenced by the interplay of mean, variance, and correlation changes.
Conclusions:
- EWMA is a viable method for real-time detection of changes in psychological associations.
- The choice of association measure impacts detection accuracy depending on the nature of changes in the data.
- Predicting potential data changes can guide the selection of optimal association measures for improved monitoring.
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