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- W4247414892 abstract "In psychological science, researchers often pay particular attention to the distinction between within- and between-person relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-person distinction and causal inference, and show that the distinction is informative but does not play a decisive role for causal inference. Our main points are threefold. First, within-person data are not necessary for causal inference; for example, between-person experiments can inform us about (average) causal effects. Second, within-person data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-person associations. Finally, despite not being sufficient, within-person 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 we ask, we should start with well-defined theoretical estimands which in turn determine both study design and data analysis." @default.
- W4247414892 created "2022-05-12" @default.
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- W4247414892 date "2021-10-11" @default.
- W4247414892 modified "2023-10-16" @default.
- W4247414892 title "These are not the effects you are looking for: Causality and the within-/between-person distinction in longitudinal data analysis" @default.
- W4247414892 doi "https://doi.org/10.31234/osf.io/tg4vj" @default.
- W4247414892 hasPublicationYear "2021" @default.
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