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In statistics, the Sobel test is a method of testing the significance of a mediation effect. The test is based on the work of Michael E. Sobel, a statistics professor at Columbia University in New York, NY. In mediation, the relationship between the independent variable and the dependent variable is hypothesized to be an indirect effect that exists due to the influence of a third variable (the mediator). As a result when the mediator is included in a regression analysis model with the independent variable, the effect of the independent variable is reduced and the effect of the mediator remains significant. The Sobel test is basically a specialized t test that provides a method to determine whether the reduction in the effect of the independent variable, after including the mediator in the model, is a significant reduction and therefore whether the mediation effect is statistically significant. ==Theoretical basis== File:Basic Mediation Diagram.png When evaluating a mediation effect three different regression models are examined: Model 1: ''Y''O = ''γ''1 + ''τX''I + ''ε''1 Model 2: ''X''M = ''γ''2 + ''αX''I + ''ε''2 Model 3: ''Y''O = ''γ''3 + ''τ''’''X''I + ''βX''M + ''ε''3 In these models ''Y''O is the dependent variable, ''X''I is the independent variable and ''X''M is the mediator. ''γ''1, ''γ''2, and ''γ''3 represent the intercepts for each model, while ''ε''1, ''ε''2, and ''ε''3 represent the error term for each equation. ''τ'' denotes the relationship between the independent variable and the dependent variable in model 1, while ''τ''’ denotes that same relationship in model 3 after controlling for the effect of the mediator. The terms ''αX''I and ''βX''M represent the relationship between the independent variable and the mediator, and the mediator and the dependent variable after controlling for the independent variable, respectively. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Sobel test」の詳細全文を読む スポンサード リンク
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