Stata：8大中介效应检验命令大比拼_逐步系数检验+两步法+sobel检验+khb+sgmediation+medsem等
本文主要为大家介绍常见的8种中介效应检验方法汇总，分别为：
1、逐步检验回归系数方法 2、两步法 3、sobel检验sgmediation命令 4、基于bootstrap的sobel检验 5、结构方程中介效应检验，medsem命令 6、khb方法 7、sgmediation2命令 8、med4way中介效应检验方法
知识回顾
中介效应是指变量间的影响关系（X→Y）不是直接的因果链关系，而是通过一个或一个以上变量(M)的间接影响产生的，此时我们称M为中介变量，而X通过M对Y产生的的间接影响称为中介效应。中介效应是间接效应的一种，模型中在只有一个中介变量的情况下，中介效应等于间接效应；当中介变量不止一个的情况下，中介效应不等于间接效应，此时间接效应可以是部分中介效应和（或）所有中介效应的总和。自变量X对因变量Y的影响，如果X变量通过影响M变量来影响Y变量，则M为中介变量。通常将变量经过中心化转化后，得方程
方程1 ：Y=cX+e1； 方程2 ：M=aX+e2； 方程3 ：Y= c′X+bM+e3。其中，c是X对Y的总效应，a、b是经过中介变量M的中介效应，c′是直接效应。当只有一个中介变量时，效应之间有c=c′+ab，中介效应的大小用c-c′=ab来衡量。中介效应检验过程
中介效应是间接效应，无论变量是否涉及潜变量，都可以用结构方程模型分析中介效应。步骤为：
第一步检验系统c，如果c不显著，Y与X相关不显著，停止中介效应分析，如果显著进行第二步； 第二步依次检验a，b，如果都显著，那么检验c′，c′显著，为部分中间效应模型，c′不显著，为完全中介效应模型； 如果a，b至少 有一个不显著，做Sobel检验，检验的统计量是Z = ^a^b / Sab，显著则中介效应显著，不显著则中介效应不显著。
案例应用
本例使用hsbdemo数据集，其中science作为DV, math作为IV, read作为中介变量。也就是说，模型说数学影响阅读，而阅读反过来又影响科学。这个模型可能有也可能没有太大的实际意义，但是它将允许我们演示运行一个中介效应测试的过程。我们将使用sgmediation command来完成这个任务，您可以使用findit sgmediation来下载这个命令。
该数据包括200个学生的选择的项目类型(prog， 三种类型 categorical variable), 他们的社会地位（ses 三种地位 categorical variable），写作分数（write， a continuous variable）。导入数据，然后进行查看数据
方法1、逐步检验回归系数方法
逐步检验回归系数方法分为三步：
reg science math //分析 x 和 y 之间的关系
reg read math //分析 x 和 m 之间的关系
reg science read math // 加入 m，看 x 和 y 之间的关系
操作结果为：
方法2、两步回归法 (two-step regression)
Zhao, Lynch et al. (2010)对传统的逐步检验回归系数方法提出再次思考，但其具体的步骤方法与逐步检验回归系数方法接近，只是取消了第一步中的检验自变量 x 和因变量 y 之间的关系， 代码为：
reg read math //分析 x 和 m 之间的关系
reg science read math // 加入 m，看 x 和 y 之间的关系
方法3、sobel检验--中介效应检验程序Sobel-Goodman mediation tests
语法格式为：
sgmediation depvar [if exp] [in range] , mv:(mediatorvar) iv(indvar) [ cv(covarlist) quietly ]
选项含义为：
depvar表示因变量 mv:(mediatorvar) 表示用于指定中介变量 iv(indvar) 表示用于指定自变量 cv(covarlist)表示用于指定控制变量
查看数据
edit
desc
数据如下：
. help sgmediation
. use "C:\Users\Metrics\Desktop\hsbdemo.dta", clear
(highschool and beyond (200 cases))
. desc
Contains data from C:\Users\Metrics\Desktop\hsbdemo.dta
obs: 200 highschool and beyond (200 cases)
vars: 13 30 Oct 2009 14:13
size: 10,000
--------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------
id float %9.0g
female float %9.0g fl
ses float %9.0g sl
schtyp float %9.0g scl type of school
prog float %9.0g sel type of program
read float %9.0g reading score
write float %9.0g writing score
math float %9.0g math score
science float %9.0g science score
socst float %9.0g social studies score
honors float %19.0g honlab honors english
awards float %9.0g
cid int %8.0g
--------------------------------------------------------------------------------------
Sorted by:
. set more off
进行操作为：
sgmediation science, mv(read) iv(math)
结果为：
. sgmediation science, mv(read) iv(math)
# 表示mv(read)为中介变量，iv(math)为自变量
Model with dv regressed on iv (path c)
Source | SS df MS Number of obs = 200
-------------+---------------------------------- F(1, 198) = 130.81
Model | 7760.55791 1 7760.55791 Prob > F = 0.0000
Residual | 11746.9421 198 59.3279904 R-squared = 0.3978
-------------+---------------------------------- Adj R-squared = 0.3948
Total | 19507.5 199 98.0276382 Root MSE = 7.7025
------------------------------------------------------------------------------
science | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
math | .66658 .0582822 11.44 0.000 .5516466 .7815135
_cons | 16.75789 3.116229 5.38 0.000 10.61264 22.90315
------------------------------------------------------------------------------
Model with mediator regressed on iv (path a)
# 形成路劲a
Source | SS df MS Number of obs = 200
-------------+---------------------------------- F(1, 198) = 154.70
Model | 9175.57065 1 9175.57065 Prob > F = 0.0000
Residual | 11743.8493 198 59.3123704 R-squared = 0.4386
-------------+---------------------------------- Adj R-squared = 0.4358
Total | 20919.42 199 105.122714 Root MSE = 7.7015
------------------------------------------------------------------------------
read | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
math | .724807 .0582745 12.44 0.000 .6098887 .8397253
_cons | 14.07254 3.115819 4.52 0.000 7.928087 20.21699
------------------------------------------------------------------------------
Model with dv regressed on mediator and iv (paths b and c')
Source | SS df MS Number of obs = 200
-------------+---------------------------------- F(2, 197) = 90.27
Model | 9328.73944 2 4664.36972 Prob > F = 0.0000
Residual | 10178.7606 197 51.6688353 R-squared = 0.4782
-------------+---------------------------------- Adj R-squared = 0.4729
Total | 19507.5 199 98.0276382 Root MSE = 7.1881
------------------------------------------------------------------------------
science | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
read | .3654205 .0663299 5.51 0.000 .2346128 .4962283
math | .4017207 .0725922 5.53 0.000 .2585632 .5448782
_cons | 11.6155 3.054262 3.80 0.000 5.592255 17.63875
------------------------------------------------------------------------------
Sobel-Goodman Mediation Tests
# 程序检验
Coef Std Err Z P>|Z|
Sobel .26485934 .05258136 5.037 4.726e-07
Goodman-1 (Aroian) .26485934 .05272324 5.024 5.072e-07
Goodman-2 .26485934 .05243909 5.051 4.400e-07
Coef Std Err Z P>|Z|
a coefficient = .724807 .058274 12.4378 0
b coefficient = .365421 .06633 5.50914 3.6e-08
Indirect effect = .264859 .052581 5.03713 4.7e-07
Direct effect = .401721 .072592 5.53394 3.1e-08
Total effect = .66658 .058282 11.4371 0
Proportion of total effect that is mediated: .39734065
Ratio of indirect to direct effect: .65931219
Ratio of total to direct effect: 1.6593122
In this example the mediation effect of read was statistically significant with approximately 40% of the total effect (of math onscience) being mediated.
在这个例子中，read的中介效果在统计上是显著的，通过这个可以得到（Proportion of total effect that is mediated: .39734065）大约40%的总效果(数学对科学)是被中介的。
操作案例2 如果需要加入协变量，则为如下命令
sgmediation science, mv(read) iv(math) cv(write)
结果为：
sgmediation science, mv(read) iv(math) cv(write)
Model with dv regressed on iv (path c)
Source | SS df MS Number of obs = 200
-------------+---------------------------------- F(2, 197) = 80.84
Model | 8793.36552 2 4396.68276 Prob > F = 0.0000
Residual | 10714.1345 197 54.3864694 R-squared = 0.4508
-------------+---------------------------------- Adj R-squared = 0.4452
Total | 19507.5 199 98.0276382 Root MSE = 7.3747
------------------------------------------------------------------------------
science | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
math | .4757015 .07094 6.71 0.000 .3358022 .6156009
write | .3055482 .0701157 4.36 0.000 .1672745 .443822
_cons | 10.68138 3.293391 3.24 0.001 4.186557 17.17621
------------------------------------------------------------------------------
Model with mediator regressed on iv (path a)
Source | SS df MS Number of obs = 200
-------------+---------------------------------- F(2, 197) = 96.80
Model | 10368.63 2 5184.31501 Prob > F = 0.0000
Residual | 10550.79 197 53.5573096 R-squared = 0.4956
-------------+---------------------------------- Adj R-squared = 0.4905
Total | 20919.42 199 105.122714 Root MSE = 7.3183
------------------------------------------------------------------------------
read | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
math | .5196538 .0703972 7.38 0.000 .380825 .6584826
write | .3283984 .0695792 4.72 0.000 .1911828 .4656141
_cons | 7.541599 3.26819 2.31 0.022 1.096471 13.98673
------------------------------------------------------------------------------
Model with dv regressed on mediator and iv (paths b and c')
Source | SS df MS Number of obs = 200
-------------+---------------------------------- F(3, 196) = 65.32
Model | 9752.65806 3 3250.88602 Prob > F = 0.0000
Residual | 9754.84194 196 49.7696017 R-squared = 0.4999
-------------+---------------------------------- Adj R-squared = 0.4923
Total | 19507.5 199 98.0276382 Root MSE = 7.0548
------------------------------------------------------------------------------
science | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
read | .3015317 .0686815 4.39 0.000 .1660822 .4369813
math | .3190094 .0766753 4.16 0.000 .167795 .4702239
write | .2065257 .0707644 2.92 0.004 .0669683 .3460831
_cons | 8.407353 3.192799 2.63 0.009 2.110703 14.704
------------------------------------------------------------------------------
Sobel-Goodman Mediation Tests
Coef Std Err Z P>|Z|
Sobel .15669211 .04152593 3.773 .00016107
Goodman-1 (Aroian) .15669211 .04180646 3.748 .00017822
Goodman-2 .15669211 .0412435 3.799 .00014517
Coef Std Err Z P>|Z|
a coefficient = .519654 .070397 7.38174 1.6e-13
b coefficient = .301532 .068681 4.39029 .000011
Indirect effect = .156692 .041526 3.77336 .000161
Direct effect = .319009 .076675 4.16053 .000032
Total effect = .475702 .07094 6.70569 2.0e-11
Proportion of total effect that is mediated: .32939164
Ratio of indirect to direct effect: .49118333
Ratio of total to direct effect: 1.4911833
方法4：基于bootstrap的sobel检验方法
操作案例3 bootstrap with case resampling
bootstrap r(ind_eff) r(dir_eff), reps(1000): sgmediation science, mv(read) iv(math)
estat bootstrap, percentile bc
结果为：
bootstrap r(ind_eff) r(dir_eff), reps(1000): sgmediation science, mv(read) iv(math)
(running sgmediation on estimation sample)
Bootstrap replications (1000)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
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Bootstrap results Number of obs = 200
Replications = 1,000
command: sgmediation science, mv(read) iv(math)
_bs_1: r(ind_eff)
_bs_2: r(dir_eff)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_bs_1 | .2648593 .0548346 4.83 0.000 .1573855 .3723332
_bs_2 | .4017207 .0819454 4.90 0.000 .2411106 .5623307
------------------------------------------------------------------------------
.
. estat bootstrap, percentile bc
Bootstrap results Number of obs = 200
Replications = 1000
command: sgmediation science, mv(read) iv(math)
_bs_1: r(ind_eff)
_bs_2: r(dir_eff)
------------------------------------------------------------------------------
| Observed Bootstrap
| Coef. Bias Std. Err. [95% Conf. Interval]
-------------+----------------------------------------------------------------
_bs_1 | .26485934 -.0057812 .05483462 .1520132 .3652509 (P)
| .1712486 .3799049 (BC)
_bs_2 | .40172068 .0059509 .08194541 .2422861 .563365 (P)
| .2336006 .5417721 (BC)
------------------------------------------------------------------------------
(P) percentile confidence interval
(BC) bias-corrected confidence interval
方法5 、结构方程中介效应检验
在使用结构方程模型(sem)估计完中介效应之后，我们可以使用medsem命令进一步检验中介效应。
medsem基于使用Stata的-sem命令估计的模型(包括观察到的变量或潜在变量，以及观察到的变量和潜在变量的组合)进行中介分析。有medsem使用两种方法作为其过程的基础。第一种方法是众所周知的Baron and Kenny方法，由Iacobucci等人(2007)调整用于结构方程模型。第二种方法是Zhao等人(2010)的方法。
首先下载安装该命令：
ssc install medsem，replace
命令medsem是专门用于sem命令之后计算中介效应的。
语法格式为:
medsem - Mediation analysis using structural equation modelling
medsem, indep(varname) med(varname) dep(varname) [mcreps(number) stand zlc rit rid]
选项含义为:
indep(varname)代表解释变量(X);
med(varname)代表中介变量(M);
dep(varname)代表被解释变量(Y)；
mereps(number)指定蒙特卡罗复制的数量，默认是样本的数量大小；
stand指定输出标准化的系数。当省略这一项时，默认输出非标准化系数；
zlc用于指定(Zhao et al..，2010)的中介效应估计方法，当省略这一选项时，默认是(Iacobucci et al. (2007))改进的BK方法。
选项rit用于指定输出中介效应与总效应之比，即rit, ratio of the indirect effect to the total effect
rid，即 rid用于指定输出中介效应与直接效应之比。
案例应用
. use http://www.ats.ucla.edu/stat/data/hsbdemo, clear
. qui sem (read <- math)(science <- read math)
. medsem, indep(math) med(read) dep(science) stand mcreps(5000) zlc rit rid
. use http://www.stata-press.com/data/r14/sem_sm2.dta, clear
. qui sem (Alien67->anomia67 pwless67)(Alien71->anomia71 pwless71)(SES->educ66 occstat66)(Alien67<-SES)(Alien71<-Alien67 SES)
. medsem, indep(SES) med(Alien67) dep(Alien71) stand mcreps(5000) zlc rit rid
. use http://www.stata-press.com/data/r14/sem_sm2.dta, clear
. qui sem (F1->educ66 occstat66)(F2->anomia66 pwless66)(F3->anomia67 pwless67)(F4->anomia71 pwless71)(F2 F3<-F1)(F4<-F1 F2 F3)
. medsem, indep(F1) med(F2) dep(F4)
. medsem, indep(F1) med(F3) dep(F4) zlc
. use http://www.stata-press.com/data/r14/sem_sm2.dta, clear
. qui sem (F1->educ66 occstat66)(F2->anomia66 pwless66)(F3->anomia67 pwless67)(F2<-F1)(F3<-F1 F2)
. medsem, indep(F1) med(F2) dep(F3) stand rit
1、先进行sem结构方程中介效应模型分析
. use http://www.ats.ucla.edu/stat/data/hsbdemo, clear
. qui sem (read <- math)(science <- read math)
. medsem, indep(math) med(read) dep(science) stand mcreps(5000) zlc rit rid
结果为：
. use "hsbdemo.dta"
(highschool and beyond (200 cases))
. ed
. desc
Contains data from hsbdemo.dta
Observations: 200 highschool and beyond (200 cases)
Variables: 13 30 Oct 2009 14:13
-----------------------------------------------------------------------------------------------------------------------------
Variable Storage Display Value
name type format label Variable label
-----------------------------------------------------------------------------------------------------------------------------
id float %9.0g
female float %9.0g fl
ses float %9.0g sl
schtyp float %9.0g scl type of school
prog float %9.0g sel type of program
read float %9.0g reading score
write float %9.0g writing score
math float %9.0g math score
science float %9.0g science score
socst float %9.0g social studies score
honors float %19.0g honlab honors english
awards float %9.0g
cid int %8.0g
-----------------------------------------------------------------------------------------------------------------------------
Sorted by:
. . qui sem (read <- math)(science <- read math)
. sem (read <- math)(science <- read math)
Endogenous variables
Observed: read science
Exogenous variables
Observed: math
Fitting target model:
Iteration 0: log likelihood = -2098.5822
Iteration 1: log likelihood = -2098.5822
Structural equation model Number of obs = 200
Estimation method: ml
Log likelihood = -2098.5822
-------------------------------------------------------------------------------
| OIM
| Coefficient std. err. z P>|z| [95% conf. interval]
--------------+----------------------------------------------------------------
Structural |
read |
math | .724807 .0579824 12.50 0.000 .6111636 .8384504
_cons | 14.07254 3.100201 4.54 0.000 7.996255 20.14882
------------+----------------------------------------------------------------
science |
read | .3654205 .0658305 5.55 0.000 .2363951 .4944459
math | .4017207 .0720457 5.58 0.000 .2605138 .5429276
_cons | 11.6155 3.031268 3.83 0.000 5.674324 17.55668
--------------+----------------------------------------------------------------
var(e.read)| 58.71925 5.871925 48.26811 71.43329
var(e.science)| 50.8938 5.08938 41.83548 61.91346
-------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0) = 0.00 Prob > chi2 = .
2、进行中介效应检验
medsem, indep(math) med(read) dep(science)
Significance testing of indirect effect (unstandardised)
+--------------------------------------------------------------------------+
Estimates | Delta | Sobel | Monte Carlo
|--------------------------------------------------------------------------|
Indirect effect | 0.265 | 0.265 | 0.264
Std. Err. | 0.052 | 0.052 | 0.052
z-value | 5.073 | 5.073 | 5.040
p-value | 0.000 | 0.000 | 0.000
Conf. Interval | 0.163 , 0.367 | 0.163 , 0.367 | 0.165 , 0.367
|--------------------------------------------------------------------------|
Baron and Kenny approach to testing mediation
STEP 1 - read:math (X -> M) with B=0.725 and p=0.000
STEP 2 - science:read (M -> Y) with B=0.365 and p=0.000
STEP 3 - science:math (X -> Y) with B=0.402 and p=0.000
As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
are significant the mediation is partial!
+--------------------------------------------------------------------------+
Note: to read more about this package help medsem
也可以加入其他选项进一步分析：
. medsem, indep(math) med(read) dep(science) stand mcreps(5000) zlc rit rid
Significance testing of indirect effect (standardised)
+--------------------------------------------------------------------------+
Estimates | Delta | Sobel | Monte Carlo
|--------------------------------------------------------------------------|
Indirect effect | 0.251 | 0.251 | 0.250
Std. Err. | 0.046 | 0.046 | 0.046
z-value | 5.446 | 5.501 | 5.466
p-value | 0.000 | 0.000 | 0.000
Conf. Interval | 0.160 , 0.341 | 0.161 , 0.340 | 0.161 , 0.341
|--------------------------------------------------------------------------|
Baron and Kenny approach to testing mediation
STEP 1 - read:math (X -> M) with B=0.662 and p=0.000
STEP 2 - science:read (M -> Y) with B=0.378 and p=0.000
STEP 3 - science:math (X -> Y) with B=0.380 and p=0.000
As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
are significant the mediation is partial!
Zhao, Lynch & Chen's approach to testing mediation
STEP 1 - science:math (X -> Y) with B=0.380 and p=0.000
As the Monte Carlo test above is significant, STEP 1 is
significant and their coefficients point in same direction,
you have complementary mediation (partial mediation)!
RIT = (Indirect effect / Total effect)
(0.251 / 0.631) = 0.397
Meaning that about 40 % of the effect of math
on science is mediated by read!
RID = (Indirect effect / Direct effect)
(0.251 / 0.380) = 0.659
That is, the mediated effect is about 0.7 times as
large as the direct effect of math on science!
+--------------------------------------------------------------------------+
Note: to read more about this package help medsem
方法6、khb检验方法
khb该方法是为二进制、logit和probit模型开发的，但该命令还包括其他非线性概率模型(有序和多项)和线性回归。
线性模型所描述的策略不能用于非线性概率模型，如logit和probit，因为这些模型的估计系数在不同模型之间是不可比较的。原因是这些模型的一个特性引起了模型的重新缩放:系数和误差方差没有单独识别。
khb方法解决了这个问题。它允许对GLM框架的许多模型(包括logit、probit、logit、oprobit和mlogit)的嵌套模型的效果进行比较。
khb方法主要用于logit和probit模型的各种变体。但是，它也可以用于线性回归，在这种情况下，它返回与标准技术相同的结果。因此，KHB只是一种使用单个命令进行分解的方便方法。
语法含义
khb model-type depvar key-vars || z-vars [if] [in] [weight] [ , options ]
模型类型可以是regression、logit、logit、probit、oprobit、cloglog、{help logit}、scobit、rologit、clogit、xtlogit、xtprobit和mlogit中的任何一种。其他模型也可能产生输出，但目前这种输出被认为是“实验性的”。
depvar是因变量的名称， key-vars是包含要分解的变量名称的变量列表， z-vars是包含感兴趣的控制变量名称的变量列表。 Factor variables允许使用因子变量。z变量的因子变量只允许在Stata 12或更高版本中使用。如果指定了option -xstandard-，则不允许key-vars使用因子变量。如果在指定的模型类型中允许，则允许使用aweights, fweights, iweights, and pweights Concomitant(varlist)指定控制变量不是中介变量，允许因子变量。 Disentangle请求一个表，该表显示每个控制变量提供的完整模型（总效应）和简化模型（直接效应）之间的差异有多大。 Summary请求所有自变量的分解情况。 默认情况下，khb报告完整模型和简化模型的效果、它们的差异以及它们的标准误。通过Summary选项，khb还提供了一个表，显示混淆比率（confounding ratios）、由于混杂而减少的百分比和缩放因子（rescale factor）。
操作应用
本例使用hsbdemo数据集，其中science作为DV, math作为IV, read作为中介变量。也就是说，模型说数学影响阅读，而阅读反过来又影响科学。这个模型可能有也可能没有太大的实际意义，但是它将允许我们演示运行一个中介效应测试的过程。我们将使用sgmediation command来完成这个任务，您可以使用findit sgmediation来下载这个命令。
该数据包括200个学生的选择的项目类型(prog， 三种类型 categorical variable), 他们的社会地位（ses 三种地位 categorical variable），写作分数（write， a continuous variable）。导入数据，然后进行查看数据据，
edit
desc
数据如下：
. use "C:\Users\Metrics\Desktop\hsbdemo.dta", clear
(highschool and beyond (200 cases))
. desc
Contains data from C:\Users\Metrics\Desktop\hsbdemo.dta
obs: 200 highschool and beyond (200 cases)
vars: 13 30 Oct 2009 14:13
size: 10,000
--------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------------
id float %9.0g
female float %9.0g fl
ses float %9.0g sl
schtyp float %9.0g scl type of school
prog float %9.0g sel type of program
read float %9.0g reading score
write float %9.0g writing score
math float %9.0g math score
science float %9.0g science score
socst float %9.0g social studies score
honors float %19.0g honlab honors english
awards float %9.0g
cid int %8.0g
--------------------------------------------------------------------------------------
Sorted by:
. set more off
进行操作为：
khb regress science math || read
结果为：
khb regress science math || read
Decomposition using Linear Probability Models
Model-Type: regress Number of obs = 200
Variables of Interest: math R-squared = 0.48
Z-variable(s): read
------------------------------------------------------------------------------
science | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
math |
Reduced | .66658 .0543901 12.26 0.000 .5599773 .7731827
Full | .4017207 .0725922 5.53 0.000 .2594427 .5439987
Diff | .2648593 .0525382 5.04 0.000 .1618863 .3678324
------------------------------------------------------------------------------
.
加入选项,结果为：
khb regress science math || read,disentangle summary
Decomposition using Linear Probability Models
Model-Type: regress Number of obs = 200
Variables of Interest: math R-squared = 0.48
Z-variable(s): read
------------------------------------------------------------------------------
science | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
math |
Reduced | .66658 .0543901 12.26 0.000 .5599773 .7731827
Full | .4017207 .0725922 5.53 0.000 .2594427 .5439987
Diff | .2648593 .0525382 5.04 0.000 .1618863 .3678324
------------------------------------------------------------------------------
Summary of confounding
Variable | Conf_ratio Conf_Pct Resc_Fact
-------------+-------------------------------------
math | 1.6593122 39.73 1
---------------------------------------------------
Components of Difference
Z-Variable | Coef Std_Err P_Diff P_Reduced
-------------+---------------------------------------------
math |
read | .2648593 .0525382 100.00 39.73
-----------------------------------------------------------
.
执行上述命令后,输出三组结果。
最上边一组报告了总效应(Reduced)系数、直接效应(Full)系数和间接效应(Diff)系数。 中间一组报告了中介变量的中介比例 最下边一组则报告了中介变量的间接效应系数和中介的比例
综上，总效应(Reduced)系数为 .66658 、直接效应(Full)系数 .4017207和间接效应(Diff)系数 .2648593。
方法7、sgmediation2
语法格式为：
sgmediation2 depvar [if exp] [in range] , iv( focus_iv ) mv( mediator_var ) [options]
其中 depvar表示因变量iv(focus_iv)表示自变量mv( mediator_var )表示中介变量
下载安装方法
net install sgmediation2, from("https://tdmize.github.io/data/sgmediation2")
help sgmediation2
操作案例为：
在这个界面可以详细的查看帮助文件手册以及案例
计算得到总效应，路径c
计算得到间接效应系数a
计算得到间接效应系数b，以及直接效应
sobel检验结果，其中间接效应为a*b
方法8、med4way
安装和数据下载命令如下：
. net install med4way, from("https://raw.githubusercontent.com/anddis/med4way/master/") replace
. help med4way
*-下载范例数据
. net get med4way, from("https://raw.githubusercontent.com/anddis/med4way/master/")
语法结构如下：
med4way depavr varlist [if] [in], a0(real) a1(real) m(real) yreg(string) mreg(string)
depvar表示被解释变量；varlist：依次为暴露因素、中介变量 、多个混杂因素（可有可无，视情况而定）
未完待续！
