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Making Valid Causal Inferences About Corrective Actions by Parents From Longitudinal Data
Author(s) -
Larzelere Robert E.,
Cox, Ronald B.
Publication year - 2013
Publication title -
journal of family theory and review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.454
H-Index - 17
eISSN - 1756-2589
pISSN - 1756-2570
DOI - 10.1111/jftr.12020
Subject(s) - causal inference , psychology , causality (physics) , selection (genetic algorithm) , longitudinal study , selection bias , longitudinal data , causal model , external validity , research design , econometrics , cognitive psychology , computer science , social psychology , statistics , machine learning , mathematics , data mining , physics , quantum mechanics
As a result of an inherent selection bias, most longitudinal analyses are biased against corrective actions that parents use to address perceived child problems. This bias can lead to unjustified or even counterproductive recommendations about corrective parental actions. To overcome this bias, this article summarizes current scholarship on improving the validity of causal inferences. Enhancing research designs is preferred, using quasi‐experimental design components and natural experiments. Comparing a typical longitudinal design with a one‐group pre‐post design shows how longitudinal designs can be improved to enhance causal validity. Perfect statistical controls for confounds or perfect instrumental variables to circumvent them could produce unbiased causal evidence. Strategies to approximate that ideal are summarized, as well as methods to check for the adequacy of those approximations .