7,129
个编辑
更改
跳到导航
跳到搜索
第136行：
第136行： − − It is possible to have statistically significant indirect effects in the absence of a total effect. This can be explained by the presence of several mediating paths that cancel each other out, and become noticeable when one of the cancelling mediators is controlled for. This implies that the terms 'partial' and 'full' mediation should always be interpreted relative to the set of variables that are present in the model. − − 在没有全面影响的情况下，有可能产生统计上显著的间接影响。这可以通过存在几个相互抵消的调解路径来解释，并且在控制其中一个取消调解器时变得显而易见。这意味着“部分”和“完全”中介术语应该始终相对于模型中存在的一组变量进行解释。 − − − − In all cases, the operation of "fixing a variable" must be distinguished from that of "controlling for a variable," which has been inappropriately used in the literature. 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. − − 在所有情况下，“固定一个变量”的操作必须与“控制一个变量”的操作区分开来，后者在文献中被不恰当地使用。检测小效应需要1000个样本量，检测中等效应需要100个样本量，检测大效应需要50个样本量。 − − − − − − The bootstrapping method provides some advantages to the Sobel's test, primarily an increase in power. The Preacher and Hayes Bootstrapping method is a non-parametric test (See Non-parametric statistics for a discussion on non-parametric tests and their power). As such, the bootstrap method does not violate assumptions of normality and is therefore recommended for small sample sizes. − − 自举方法为 Sobel 的测试提供了一些优点，主要是增加了功耗。传道者和 Hayes Bootstrapping 方法是一种非参数检验(参见无母数统计非参数检验及其效力的讨论)。因此，自助法并不违反常态假设，因此建议小样本情况下使用。 − − Bootstrapping involves repeatedly randomly sampling observations with replacement from the data set to compute the desired statistic in each resample. Computing over hundreds, or thousands, of bootstrap resamples provide an approximation of the sampling distribution of the statistic of interest. Hayes offers a macro <http://www.afhayes.com/> that calculates bootstrapping directly within SPSS, a computer program used for statistical analyses. This method provides point estimates and confidence intervals by which one can assess the significance or nonsignificance of a mediation effect. Point estimates reveal the mean over the number of bootstrapped samples and if zero does not fall between the resulting confidence intervals of the bootstrapping method, one can confidently conclude that there is a significant mediation effect to report. − − 自举涉及重复随机抽样观察和替换从数据集计算期望的统计量在每个重采样。通过计算数百个或数千个引导重采样提供了感兴趣统计量的抽样分布的近似值。提供了一个宏 < http://www.afhayes.com/ > ，它可以直接计算 SPSS 中的自举，这是一个用于统计分析的计算机程序。这种方法提供了点估计和置信区间，通过这些点估计和置信区间可以评估中介效应的重要性或非重要性。点估计揭示了引导样本数目的平均值，如果零没有落在引导方法产生的置信区间之间，人们可以自信地得出结论，有一个重要的中介效应要报告。 − − − − − − As outlined above, there are a few different options one can choose from to evaluate a mediation model. − − 如上所述，在评估一个中介模型时，可以从几个不同的选项中进行选择。 − − − − Bootstrapping is becoming the most popular method of testing mediation because it does not require the normality assumption to be met, and because it can be effectively utilized with smaller sample sizes (N < 25). However, mediation continues to be most frequently determined using the logic of Baron and Kenny or the Sobel test. It is becoming increasingly more difficult to publish tests of mediation based purely on the Baron and Kenny method or tests that make distributional assumptions such as the Sobel test. Thus, it is important to consider your options when choosing which test to conduct. and interpreted formally. − − 引导法因为不需要满足正态性假设，并且在样本量较小时(n < 25)可以有效地利用，因此成为最流行的检验中介的方法。然而，调解仍然是最常用的决定使用的逻辑的巴伦和肯尼或索贝尔测试。发布纯粹基于 Baron 和 Kenny 方法的调解测试或者进行分布性假设(如 Sobel 测试)的测试变得越来越困难。因此，在选择进行哪项测试时，考虑你的选择是很重要的。并正式解释。 − − − − (1) Experimental-causal-chain design − − (1)实验因果链设计 − − − − An experimental-causal-chain design is used when the proposed mediator is experimentally manipulated. Such a design implies that one manipulates some controlled third variable that they have reason to believe could be the underlying mechanism of a given relationship. − − 实验因果链设计时，所提出的中介体是实验操纵。这样的设计意味着一个人操纵一些他们有理由相信可能是给定关系的潜在机制的受控的第三变量。 − − − − (2) Measurement-of-mediation design − − (2)测量调解设计 − − − − A measurement-of-mediation design can be conceptualized as a statistical approach. Such a design implies that one measures the proposed intervening variable and then uses statistical analyses to establish mediation. This approach does not involve manipulation of the hypothesized mediating variable, but only involves measurement. − − 度量中介设计可以概念化为统计方法。这样的设计意味着一个人衡量所提议的中间变量，然后使用统计分析来建立调解。这种方法不涉及对虚拟中介变量的操作，而只涉及测量。 − − − −
→Baron and Kenny's (1986) steps for mediation
'''Example'''
'''Example'''
The following example, drawn from Howell (2009),<ref>Howell, D. C. (2009). Statistical methods for psychology (7th ed.). Belmot, CA: Cengage Learning.</ref> 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]].
The following example, drawn from Howell (2009),<ref>Howell, D. C. (2009). Statistical methods for psychology (7th ed.). Belmot, CA: Cengage Learning.</ref> 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:
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 (i.e., independent variable) predicts how confident you feel about parenting your own children (i.e., dependent variable).
:How you were parented <math> \to </math> confidence in own parenting abilities.
:How you were parented <math> \to </math> confidence in own parenting abilities.
Step 2:
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 (i.e., independent variable) predicts your feelings of competence and self-esteem (i.e., mediator).
:How you were parented <math> \to </math> Feelings of competence and self-esteem.
:How you were parented <math> \to </math> Feelings of competence and self-esteem.
Step 3:
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).
: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.
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 <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).
==Direct versus indirect effects==
==Direct versus indirect effects==