@article{ref1,
title="These are not the effects you are looking for: causality and the within-/between-persons distinction in longitudinal data analysis",
journal="Advances in methods and practices in psychological science",
year="2023",
author="Rohrer, Julia M. and Murayama, Kou",
volume="6",
number="1",
pages="e25152459221140842-e25152459221140842",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="2515-2459",
doi="10.1177/25152459221140842",
url="http://dx.doi.org/10.1177/25152459221140842"
}