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第274行： − − must go beyond regression analysis, and should − − 必须超越回归分析 − − − invoke an operation that mimics "fixing M", − − 调用一个模仿“修理 m”的操作, − − − − rather than "conditioning on M." − − 而不是“对 m 施加条件作用” − − − − Such an operator, denoted do(M = m), was defined in Pearl (1994) or "structural counterfactuals". − − 这样一个算子，表示 do (m = m) ，在 Pearl (1994)或“结构反事实”中定义。 − − − − These new variables provide convenient notation − − 这些新的变量提供了方便的符号
→Sobel's test
==Sobel's test==
==Sobel's test==
{{main|Sobel test}}
{{main|Sobel test}}
As mentioned above, [[Sobel test|Sobel's test]]<ref name="Sobel, M. E. 1982 pp. 290"/> is performed to determine if the relationship between the independent variable and dependent variable has been significantly reduced after inclusion of the mediator variable. In other words, this test assesses whether a mediation effect is significant. It examines the relationship between the independent variable and the dependent variable compared to the relationship between the independent variable and dependent variable including the mediation factor.
As mentioned above, [[Sobel test|Sobel's test]]<ref name="Sobel, M. E. 1982 pp. 290"/> is performed to determine if the relationship between the independent variable and dependent variable has been significantly reduced after inclusion of the mediator variable. In other words, this test assesses whether a mediation effect is significant. It examines the relationship between the independent variable and the dependent variable compared to the relationship between the independent variable and dependent variable including the mediation factor.
The Sobel test is more accurate than the Baron and Kenny steps explained above; however, it does have low statistical power. As such, large sample sizes are required in order to have sufficient power to detect significant effects. This is because the key assumption of Sobel's test is the assumption of normality. Because Sobel's test evaluates a given sample on the normal distribution, small sample sizes and skewness of the sampling distribution can be problematic (see [[Normal distribution]] for more details). Thus, the rule of thumb as suggested by MacKinnon et al., (2002) <ref>{{cite journal | last1 = MacKinnon | first1 = D. P. | last2 = Lockwood | first2 = C. M. | last3 = Lockwood | first3 = J. M. | last4 = West | first4 = S. G. | last5 = Sheets | first5 = V. | year = 2002 | title = A comparison of methods to test mediation and other intervening variable effects | journal = Psychological Methods| volume = 7 | issue = 1| pages = 83–104 | doi=10.1037/1082-989x.7.1.83| pmid = 11928892 | pmc=2819363}}</ref> is that a sample size of 1000 is required to detect a small effect, a sample size of 100 is sufficient in detecting a medium effect, and a sample size of 50 is required to detect a large effect.
The Sobel test is more accurate than the Baron and Kenny steps explained above; however, it does have low statistical power. As such, large sample sizes are required in order to have sufficient power to detect significant effects. This is because the key assumption of Sobel's test is the assumption of normality. Because Sobel's test evaluates a given sample on the normal distribution, small sample sizes and skewness of the sampling distribution can be problematic (see [[Normal distribution]] for more details). Thus, the rule of thumb as suggested by MacKinnon et al., (2002) <ref>{{cite journal | last1 = MacKinnon | first1 = D. P. | last2 = Lockwood | first2 = C. M. | last3 = Lockwood | first3 = J. M. | last4 = West | first4 = S. G. | last5 = Sheets | first5 = V. | year = 2002 | title = A comparison of methods to test mediation and other intervening variable effects | journal = Psychological Methods| volume = 7 | issue = 1| pages = 83–104 | doi=10.1037/1082-989x.7.1.83| pmid = 11928892 | pmc=2819363}}</ref> is that a sample size of 1000 is required to detect a small effect, a sample size of 100 is sufficient in detecting a medium effect, and a sample size of 50 is required to detect a large effect.
==Preacher and Hayes (2004) bootstrap method==
==Preacher and Hayes (2004) bootstrap method==