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第38行：
第38行： − Step 1: − − − − − How you were parented (i.e., independent variable) predicts how confident you feel about parenting your own children (i.e., dependent variable). − − 你是如何被养育的(即，自变量)预测了你对养育自己孩子的信心(即，因变量)。 − − − How you were parented <math> \to </math> confidence in own parenting abilities. − − 你是如何培养孩子对自己养育能力的信心的。 第61行：
第48行： − Step 2: + − − − − '''Step 2:''' − − How you were parented (i.e., independent variable) predicts your feelings of competence and self-esteem (i.e., mediator). − − 你是如何被抚养的(即，独立变量)可以预测你的能力和自尊的感受(即，调解人)。 − − − − − How you were parented <math> \to </math> Feelings of competence and self-esteem. − − 你是如何培养自己的能力和自尊心的。 − − − − Step 3: − − 第三步: − − 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). − − 你的能力和自尊感(即中介者)能够预测你对抚养自己的孩子的自信程度(即因变量) ，同时控制你是如何被抚养的(即自变量)。 − + − 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.+ − − 这些发现会导致这样的结论，即你的能力和自尊感会调节你是如何被抚养的和你对抚养自己的孩子有多自信之间的关系。 − − − − − 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 for more info). − − 注意: 如果步骤1没有产生显著的结果，一个人可能仍然有理由移动到步骤2。有时，独立变量和因变量之间确实存在显著的关系，但是由于样本量小，或者其他额外的因素，没有足够的能量来预测实际存在的影响(参见 Shrout & Bolger，2002)。 − − − − Direct Effect in a Mediation Model − − 调解模式中的直接效果 − − In the diagram shown above, the indirect effect is the product of path coefficients "A" and "B". The direct effect is the coefficient " C' ". − − 在上图中，间接效应是路径系数“ a”和“ b”的乘积。直接的影响是系数“ c”。 − − The direct effect measures the extent to which the dependent variable changes when the independent variable increases by one unit and the mediator variable remains unaltered. In contrast, the indirect effect measures the extent to which the dependent variable changes when the independent variable is held fixed and the mediator variable changes by the amount it would have changed had the independent variable increased by one unit. The effect of an independent variable on the dependent variable can become nonsignificant when the mediator is introduced simply because a trivial amount of variance is explained (i.e., not true mediation). Thus, it is imperative to show a significant reduction in variance explained by the independent variable before asserting either full or partial mediation. − − 直接效应衡量的是当自变量增加一个单位而中介变量保持不变时，因变量变化的程度。相比之下，间接效应衡量的是因变量在自变量固定和中介变量增加一个单位时变化的程度。在引入调解人时，因为只解释了微小的方差(即，不是真正的调解) ，所以自变量对因变量的影响可能变得不重要。因此，在进行全部或部分调解之前，必须显示由独立变量解释的差异显著减少。 第158行：
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→Baron and Kenny's (1986) steps for mediation
'''Step 1:'''
'''Step 1:'''
第一步:
第一步:
:Regress the dependent variable on the independent variable to confirm that the independent variable is a significant predictor of the dependent variable.
:Regress the dependent variable on the independent variable to confirm that the independent variable is a significant predictor of the dependent variable.
: Independent variable <math> \to </math> dependent variable
: Independent variable <math> \to </math> dependent variable
:: <math>Y=\beta_{10} +\beta_{11}X + \varepsilon_1</math>
:: <math>Y=\beta_{10} +\beta_{11}X + \varepsilon_1</math>
* ''β''<sub>11</sub> is significant
* ''β''<sub>11</sub> is significant
'''Step 2:'''
第二步:
第二步:
:Regress the mediator on the independent variable to 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.
:Regress the mediator on the independent variable to 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 <math> \to </math> mediator
: Independent variable <math> \to </math> mediator
:: <math>Me=\beta_{20} +\beta_{21}X + \varepsilon_2</math>
:: <math>Me=\beta_{20} +\beta_{21}X + \varepsilon_2</math>
* ''β''<sub>21</sub> is significant
* ''β''<sub>21</sub> is significant
'''Step 3:'''
'''Step 3:'''
Step 3:
第三步:
:Regress the dependent variable on both the mediator and independent variable to confirm that a) the mediator is a significant predictor of the dependent variable, and b) the strength of the coefficient of the previously significant independent variable in Step #1 is now greatly reduced, if not rendered nonsignificant.
:Regress the dependent variable on both the mediator and independent variable to confirm that a) the mediator is a significant predictor of the dependent variable, and b) the strength of the coefficient of the previously significant independent variable in Step #1 is now greatly reduced, if not rendered nonsignificant.
:: <math>Y=\beta_{30} +\beta_{31}X +\beta_{32}Me + \varepsilon_3</math>
:: <math>Y=\beta_{30} +\beta_{31}X +\beta_{32}Me + \varepsilon_3</math>
* ''β''<sub>32</sub> is significant
* ''β''<sub>32</sub> is significant
* ''β''<sub>31</sub> should be smaller in absolute value than the original effect for the independent variable (β<sub>11</sub> above)
* ''β''<sub>31</sub> should be smaller in absolute value than the original effect for the independent variable (β<sub>11</sub> above)
'''Example'''
'''Example'''
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 <ref>{{cite journal | last1 = Shrout | first1 = P. E. | last2 = Bolger | first2 = N. | year = 2002 | title = Mediation in experimental and nonexperimental studies: New procedures and recommendations | journal = Psychological Methods | volume = 7 | issue = 4| pages = 422–445 | doi=10.1037/1082-989x.7.4.422}}</ref> for more info).
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 <ref>{{cite journal | last1 = Shrout | first1 = P. E. | last2 = Bolger | first2 = N. | year = 2002 | title = Mediation in experimental and nonexperimental studies: New procedures and recommendations | journal = Psychological Methods | volume = 7 | issue = 4| pages = 422–445 | doi=10.1037/1082-989x.7.4.422}}</ref> for more info).
注意: 如果步骤1没有产生显著的结果，一个人可能仍然有理由移动到步骤2。有时，独立变量和因变量之间确实存在显著的关系，但是由于样本量小，或者其他额外的因素，没有足够的能量来预测实际存在的影响(参见 Shrout & Bolger，2002)。
==Direct versus indirect effects==
==Direct versus indirect effects==