Establishing Causality Using Longitudinal Hierarchical Linear Modeling: An Illustration Predicting Achievement From Self-Control

• Published in: Social psychological & personality science Vol. 1; no. 4; pp. 311 - 317
• Main Authors: Duckworth, Angela Lee, Tsukayama, Eli, May, Henry
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.10.2010; SAGE PUBLICATIONS, INC
• Subjects: Causality; Change agents; Ethnicity; Intelligence quotient; Intelligence tests; Linear analysis; Linear models; Predictive validity; Self control; Self esteem; Selfcontrol; Selfesteem; self-control; self-regulation; academic achievement; causal modeling
• ISSN: 1948-5506; 1948-5514
• DOI: 10.1177/1948550609359707

The predictive validity of personality for important life outcomes is well established, but conventional longitudinal analyses cannot rule out the possibility that unmeasured third-variable confounds fully account for the observed relationships. Longitudinal hierarchical linear models (HLM) with time-varying covariates allow each subject to serve as his or her own control, thus eliminating between-individual confounds. HLM also allow the directionality of the causal relationship to be tested by reversing time-lagged predictor and outcome variables. We illustrate these techniques through a series of models that demonstrate that within-individual changes in self-control over time predict subsequent changes in GPA but not vice-versa. The evidence supporting a causal role for self-control was not moderated by IQ, gender, ethnicity, or income. Further analyses rule out one time-varying confound: self-esteem. The analytic approach taken in this study provides the strongest evidence to date for the causal role of self-control in determining achievement.