Establishing Causality Using Longitudinal Hierarchical Linear Modeling: An Illustration Predicting Achievement From Self-Control.
Establishing Causality Using Longitudinal Hierarchical Linear Modeling: An Illustration Predicting Achievement From Self-Control.
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DOI：
10.1177/1948550609359707
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发表时间：
2010-10-01
影响因子：
5.7
通讯作者：
May H
中科院分区：
文献类型：
--
作者：
Duckworth AL;Tsukayama E;May H
人格对重要生活结果的预测有效性已经确立，但传统的纵向分析不能排除不可测量的第三变量混淆完全解释所观察到的关系的可能性。纵向分层线性模型（HLM）随时间变化的协变量允许每个主题作为他或她自己的控制，从而消除个体之间的混淆。HLM还允许通过逆转时滞预测变量和结局变量来测试因果关系的方向性。我们通过一系列模型来说明这些技术，这些模型表明，随着时间的推移，自我控制的个体内变化可以预测GPA的后续变化，但反之亦然。支持自我控制的因果作用的证据不受智商、性别、种族或收入的影响。进一步的分析排除了一个随时间变化的混淆因素：自尊。本研究采用的分析方法为自我控制在决定成就中的因果作用提供了迄今为止最有力的证据。
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 allows 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.
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影响因子：
16.4
作者：
CRONBACH, LJ
通讯作者：
CRONBACH, LJ
影响因子：
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作者：
MISCHEL, W;SHODA, Y;RODRIGUEZ, ML
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作者：
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