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In statistics, a mediation model is one that seeks to identify and explicate the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable. Rather than hypothesizing a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables.〔MacKinnon, D. P. (2008). ''Introduction to Statistical Mediation Analysis''. New York: Erlbaum.〕 In other words, mediating relationships occur when a third variable plays an important role in governing the relationship between the other two variables. Researchers are now focusing their studies on better understanding known findings. Mediation analyses are employed to understand a known relationship by exploring the underlying mechanism or process by which one variable (''X'') influences another variable (''Y'') through a mediator (''M'').〔Cohen, J.; Cohen, P.; West, S. G.; Aiken, L. S. (2003) ''Applied multiple regression/correlation analysis for the behavioral sciences'' (3rd ed.). Mahwah, NJ: Erlbaum.〕 In other words, X leads to M leads to Y. For example, if gender is thought to be the cause of some characteristic, one assumes that other social or biological mechanisms associated with gender can explain how gender-associated differences arise. Such an intervening variable is called a mediator. ==Baron and Kenny's (1986) Steps for Mediation== Baron and Kenny (1986) 〔Baron, R. M. and Kenny, D. A. (1986) "The Moderator-Mediator Variable Distinction in Social Psychological Research – Conceptual, Strategic, and Statistical Considerations", Journal of Personality and Social Psychology, Vol. 51(6), pp. 1173–1182.〕 laid out several requirements that must be met to form a true mediation relationship. They are outlined below using a real world example. See the diagram above for a visual representation of the overall mediating relationship to be explained. Step 1: :Regress the dependent variable on the independent variable. In other words, confirm that the independent variable is a significant predictor of the dependent variable. Independent Variable Dependent Variable : * ''β11'' is significant Step 2: :Regress the mediator on the independent variable. In other words, confirm that the independent variable is a significant predictor of the mediator. If the mediator is not associated with the independent variable, then it couldn’t possibly mediate anything. Independent Variable Mediator : * ''β21'' is significant Step 3: :Regress the dependent variable on both the mediator and independent variable. In other words, confirm that the mediator is a significant predictor of the dependent variable, while controlling for the independent variable. This step involves demonstrating that when the mediator and the independent variable are used simultaneously to predict the dependent variable, the previously significant path between the independent and dependent variable (Step #1) is now greatly reduced, if not nonsignificant. : * ''β32'' is significant * ''β31'' should be smaller in absolute value than the original mediation effect (β11 above) Example The following example, drawn from Howell (2009),〔Howell, D. C. (2009). Statistical methods for psychology (7th ed.). Belmot, CA: Cengage Learning.〕 explains each step of Baron and Kenny’s requirements to understand further how a mediation effect is characterized. Step 1 and step 2 use simple regression analysis, whereas step 3 uses multiple regression analysis. Step 1: :How you were parented (i.e., independent variable) predicts how confident you feel about parenting your own children (i.e., dependent variable). How you were parented Confidence in own parenting abilities. Step 2: :How you were parented (i.e., independent variable) predicts your feelings of competence and self-esteem (i.e., mediator). How you were parented Feelings of competence and self-esteem. Step 3: :Your feelings of competence and self-esteem (i.e., mediator) predict how confident you feel about parenting your own children (i.e., dependent variable), while controlling for how you were parented (i.e., independent variable). Such findings would lead to the conclusion implying that your feelings of competence and self-esteem mediate the relationship between how you were parented and how confident you feel about parenting your own children. Note: If step 1 does not yield a significant result, one may still have grounds to move to step 2. Sometimes there is actually a significant relationship between independent and dependent variables but because of small sample sizes, or other extraneous factors, there could not be enough power to predict the effect that actually exists (See Shrout & Bolger, 2002 〔Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422-445〕 for more info). 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「Mediation (statistics)」の詳細全文を読む スポンサード リンク
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