Methods | Special Issue: Network Psychometrics

Observed correlations between person-means depend on within-person correlations

Jonas Haslbeck

https://doi.org/10.56296/aip00020
Published: September 20, 2024
Copyright: The authors (CC BY 4.0)

Haslbeck, J. & Epskamp, S. (2024). Observed correlations between person-means depend on within-person correlations. advances.in/psychology, 2, e853425. https://doi.org/10.56296/aip00020

Haslbeck, Jonas, and Sacha Epskamp. "Observed correlations between person-means depend on within-person correlations." advances.in/psychology, vol. 2, no. 1, 2024, e853425. https://doi.org/10.56296/aip00020.

Haslbeck, Jonas, and Sacha Epskamp. 2024. "Observed correlations between person-means depend on within-person correlations." advances.in/psychology 2 (1): e853425. https://doi.org/10.56296/aip00020.

Haslbeck J, Epskamp S. Observed correlations between person-means depend on within-person correlations. advances.in/psychology. 2024;2(1):e853425. doi:10.56296/aip00020.

Haslbeck, J. and Epskamp, S. (2024) 'Observed correlations between person-means depend on within-person correlations', advances.in/psychology, 2(1), e853425. Available at: https://doi.org/10.56296/aip00020.

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Intensive longitudinal data have become a common data type across psychological disciplines. A key issue in the analysis of such data is the separation of within-person and between-person effects. This problem is well studied for the effect of between-person effects (e.g., varying intercepts) on within-person parameters (e.g., cross-lagged effects). In this paper, we discuss a less appreciated effect of within-person correlations on correlations between person-wise means. Using simulations and an analytical derivation, we show how observed correlations between person-wise means are a function of both population between-person correlations and within-person correlations. This has implications for the interpretation of statistical relationships between person-wise means, for example when estimated directly from the data or within stepwise approaches to estimating multilevel vector autoregressive models, such as in the popular R package mlVAR. We discuss implications for applied research and possible strategies to avoid this problem.

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