These are not the effects you are looking for: causality and the within-/between-person distinction in longitudinal data analysis

Rohrer, J. M. and Murayama, K. (2023) These are not the effects you are looking for: causality and the within-/between-person distinction in longitudinal data analysis. Advances in Methods and Practices in Psychological Science, 6 (1). pp. 1-14. ISSN 2515-2459 doi: 10.1177/25152459221140842

Abstract/Summary

In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-persons associations. Finally, despite not being sufficient, within-persons data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions researchers ask, researchers should start with well-defined theoretical estimands, which in turn determine both study design and data analysis.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/108581
Identification Number/DOI	10.1177/25152459221140842
Refereed	Yes
Divisions	Life Sciences > School of Psychology and Clinical Language Sciences > Department of Psychology
Publisher	SAGE
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