How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study

Does X affect Y? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel mode...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Sociological methods & research Ročník 51; číslo 2; s. 837 - 865
Hlavní autoři: Leszczensky, Lars, Wolbring, Tobias
Médium: Journal Article
Jazyk:angličtina
Vydáno: Los Angeles, CA SAGE Publications 01.05.2022
SAGE PUBLICATIONS, INC
Témata:
ISSN:0049-1241, 1552-8294
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Does X affect Y? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel models. Whereas the methodological literature has suggested various alternative solutions, these approaches face many criticisms, chief among them to be sensitive to the correct specification of temporal lags. Applied researchers are thus left with little guidance. Seeking to provide such guidance, we compare how different panel models perform under a range of different conditions. Our Monte Carlo simulations reveal that unlike conventional panel models, a cross-lagged panel model with fixed effects not only offers protection against bias arising from reverse causality under a wide range of conditions but also helps to circumvent the problem of misspecified temporal lags.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0049-1241
1552-8294
DOI:10.1177/0049124119882473