SPSS+AMOS数据分析案例教程-关于中介模
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SPSS+AMOS数据分析案例教程-关于中介模
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倒U关系回归分析中介效应和调节效应分析SPSS视频教程
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stata教程08-中介效应分析

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2018年12月22日
文章目录
  1. 1. 中介分析原理
  2. 2. 本案例的数据介绍
  3. 3. 过滤缺失值
  4. 4. 回归1: 自变量预测因变量
  5. 5. 回归2: 自变量预测中介变量
  6. 6. 回归3: 自变量和中介变量预测因变量
  7. 7. 结论
  8. 8. 使用插件

中介分析原理

下面是我之前写过的关于中介效应的文章, 大家看后就知道原理了:

May 2015 SPSS实例：[16]中介效应的检验过程 Feb 2016 SPSS实例：[18]中介效应占总效应百分比 Jan 2016 SPSS实例：[20]检验中介效应的操作方法 Oct 2016 SPSS实例：[17]进行sobel检验(小白教程) Oct 2016 在线绘制中介效应图

在这里我重新声明一下具体的过程:

下面的回归模型中都带有控制变量，只不过为了简洁，没有在下面描述。首先使用自变量ind预测因变量dep, 得到模型1(dep=c1 * ind +e1), 然后使用自变量ind预测中介变量med, 得到模型2(med=a * ind +e2), 最后使用自变量ind和中介变量med预测因变量dep, 得到模型3(dep=b* med + c2 * ind + e3)。

本案例的数据介绍

本案例使用的是自己编制的数据，自变量就是ind, 因变量就是dep, 中介变量就是med, 其他控制变量都以control+数字的格式命名。

下面加载这个数据:

1
use "data/mediator-data.dta", clear

过滤缺失值

我们需要做三个回归分析, 但是因为回归分析涉及的变量不同, 如果变量存在缺失值, 那么很有可能造成三个回归方程使用的观测数据有差异(因为有不同的缺失值), 所以我们再做回归之前, 先要生成一个miss_num变量, 如果自变量/中介变量/因变量/控制变量都没有缺失, 那么miss_num=0, 否则miss_num>0。

1
egen miss_num = rowmiss(dep med ind control1 control2 control3 control4 control5)

看一下缺失情况: 从下表可以看出, 没有缺失的有861个样本。

1
tab miss_num
输出(stream):
miss_num | Freq. Percent Cum. ------------+----------------------------------- 0 | 861 73.72 73.72 1 | 298 25.51 99.23 2 | 1 0.09 99.32 3 | 1 0.09 99.40 4 | 7 0.60 100.00 ------------+----------------------------------- Total | 1,168 100.00

回归1: 自变量预测因变量

dep=c1 * ind +e1

1
reg dep ind control1 control2 control3 control4 control5 if miss_num==0
输出(stream):
Source | SS df MS Number of obs = 861 -------------+---------------------------------- F(6, 854) = 2.76 Model | .459843972 6 .076640662 Prob > F = 0.0115 Residual | 23.6942548 854 .027745029 R-squared = 0.0190 -------------+---------------------------------- Adj R-squared = 0.0121 Total | 24.1540988 860 .028086161 Root MSE = .16657 ------------------------------------------------------------------------------ dep | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ind | -.0745096 .0352931 -2.11 0.035 -.1437809 -.0052382 control1 | -.0003018 .0043662 -0.07 0.945 -.0088715 .008268 control2 | -.0133247 .00578 -2.31 0.021 -.0246693 -.00198 control3 | -.0044711 .0044726 -1.00 0.318 -.0132497 .0043075 control4 | .1799002 .0983623 1.83 0.068 -.01316 .3729603 control5 | .0340114 .0191444 1.78 0.076 -.0035642 .0715871 _cons | .7167078 .2593365 2.76 0.006 .2076962 1.22572 ------------------------------------------------------------------------------

从上面的结果中可以看到, c1这个系数是显著的, c1 = -.0745096, sc1 = .0352931

回归2: 自变量预测中介变量

med=a * ind +e2

1
reg med ind control1 control2 control3 control4 control5 if miss_num == 0
输出(stream):
Source | SS df MS Number of obs = 861 -------------+---------------------------------- F(6, 854) = 2.90 Model | 3.83093084 6 .638488473 Prob > F = 0.0083 Residual | 187.834574 854 .219946808 R-squared = 0.0200 -------------+---------------------------------- Adj R-squared = 0.0131 Total | 191.665505 860 .222866867 Root MSE = .46898 ------------------------------------------------------------------------------ med | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ind | -.1950457 .0993702 -1.96 0.050 -.3900841 -7.35e-06 control1 | .0279925 .0122933 2.28 0.023 .0038638 .0521211 control2 | .0083398 .016274 0.51 0.608 -.0236019 .0402814 control3 | .0271718 .0125929 2.16 0.031 .0024551 .0518885 control4 | .8851904 .2769459 3.20 0.001 .3416161 1.428765 control5 | .0229235 .0539025 0.43 0.671 -.0828733 .1287204 _cons | 2.094221 .73018 2.87 0.004 .6610633 3.527379 ------------------------------------------------------------------------------

从上面的结果中可以看到, 这个系数是显著的, a =-.1950457, sa = .0993702

回归3: 自变量和中介变量预测因变量

dep=b* med + c2 * ind + e3

1
reg dep med ind control1 control2 control3 control4 control5 if miss_num == 0
输出(stream):
Source | SS df MS Number of obs = 861 -------------+---------------------------------- F(7, 853) = 2.99 Model | .578216371 7 .082602339 Prob > F = 0.0042 Residual | 23.5758824 853 .027638784 R-squared = 0.0239 -------------+---------------------------------- Adj R-squared = 0.0159 Total | 24.1540988 860 .028086161 Root MSE = .16625 ------------------------------------------------------------------------------ dep | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- med | -.0251037 .0121303 -2.07 0.039 -.0489124 -.0012949 ind | -.0794059 .0353048 -2.25 0.025 -.1487004 -.0101114 control1 | .000401 .004371 0.09 0.927 -.0081783 .0089802 control2 | -.0131153 .0057698 -2.27 0.023 -.02444 -.0017906 control3 | -.003789 .0044762 -0.85 0.398 -.0125746 .0049966 control4 | .2021217 .0987592 2.05 0.041 .0082821 .3959613 control5 | .0345869 .0191098 1.81 0.071 -.0029208 .0720946 _cons | .7692805 .2600831 2.96 0.003 .2588026 1.279758 ------------------------------------------------------------------------------

从上面的结果中可以看到, 这个b系数是显著的, b =–.0251037, sb = .0121303

c2系数也是显著的: c2 = -.0794059, sc2 = .0353048

结论

因为所有系数都是显著的, 所以我们可以认为中介效应是存在的, 并且属于部分中介效应。中介的效应量可以这样计算:

m = a*b

1
display -.1950457 * -.0251037
输出(stream):
.00489637

中介效应占总效应的百分比就是:

1
display (-.1950457 * -.0251037) /  -.0745096 * 100
输出(stream):
-6.5714602

使用插件

实际上我们可以把以上的代码都封装成一个命令, 恰好我在网上找到了一段代码, 可以做中介效应。你需要把以下代码保存到stata安装目录的这个路径下:

Stata15\ado\base\s, 在这个文件夹下创建一个文件名为sgmediation.ado, 把以下代码贴进去, 然后重启stata。

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*! version 1.1.1 -- 5/17/06 -- pbe
*! verion 1.0 -- 2/28/05 -- pbe
program define sgmediation
/* sobel-goodman mediation tests */
version 8.0
syntax varlist(max=1) [if/] [in], iv(varlist numeric max=1) ///
   mv(varlist numeric max=1) [ cv(varlist numeric) BOOTstrap reps(integer 200) level(integer 95)]
marksample touse
markout `touse' `varlist' `mv' `iv' `cv'
tempname coef emat

display
tabulate  `mv' if `touse'

display
display as text "Model with dv regressed on iv"
regress `varlist' `iv' `cv' if `touse'
local ccoef=_b[`iv']

display
display "Model with mediator regressed on iv"
regress `mv' `iv' `cv' if `touse'

local acoef=_b[`iv']
local avar=_se[`iv']^2

display
display "Model with dv regressed on mediator and iv"
regress `varlist' `mv' `iv' `cv' if `touse'

local bcoef=_b[`mv']
local bvar=_se[`mv']^2

local sobel =(`acoef'*`bcoef')
local serr=sqrt(`bcoef'^2*`avar' + `acoef'^2*`bvar')
local stest=`sobel'/`serr'
local g1err=sqrt(`bcoef'^2*`avar' + `acoef'^2*`bvar' + `avar'*`bvar')
local good1=`sobel'/`g1err'
local g2err=sqrt(`bcoef'^2*`avar' + `acoef'^2*`bvar' - `avar'*`bvar')
local good2=`sobel'/`g2err'
local toteff = `sobel'/((`acoef'*`bcoef')+(`ccoef'-(`acoef'*`bcoef')))
local ratio = `sobel'/((`ccoef'-(`acoef'*`bcoef')))

display
display "Sobel-Goodman Mediation Tests"
display
display "             Coef         Std Err     Z           P>|Z|"
display as txt "Sobel       " as res `sobel' _skip(4) `serr'  %8.4g ///
`stest', _skip(5) 2*(1-norm(abs(`stest')))
display as txt "Goodman-1   " as res `sobel' _skip(4) `g1err' %8.4g ///
`good1', _skip(5) 2*(1-norm(abs(`good1')))
display as txt "Goodman-2   " as res `sobel' _skip(4) `g2err' %8.4g ///
`good2', _skip(5) 2*(1-norm(abs(`good2')))
display
display as txt "Pecent of total effect that is mediated: ", as res ///
%5.2f 100*`toteff',"%"
display as txt "Ratio of indirect to direct effect:     ", as res %8.4f `ratio'

if "`bootstrap'"~="" {
  display 
  display as txt "Percentile and Bias-corrected bootstrap results for Sobel: `reps' replications"
  display

  quietly bootstrap coef=r(sobel), reps(`reps') level(`level'): sgboot `varlist' , mv(`mv') iv(`iv') cv(`cv' )
  estat bootstrap, bc percentile noheader
  }

end

最后你在使用的时候, 就可以直接调用这个命令即可:

1
sgmediation dep, mv(med) iv(ind) cv(control1 control2 control3 control4 control5  )
输出(stream):
med | Freq. Percent Cum. ------------+----------------------------------- 0 | 573 66.55 66.55 1 | 288 33.45 100.00 ------------+----------------------------------- Total | 861 100.00 med | Freq. Percent Cum. ------------+----------------------------------- 0 | 730 62.50 62.50 1 | 438 37.50 100.00 ------------+----------------------------------- Total | 1,168 100.00 Model with dv regressed on iv Source | SS df MS Number of obs = 861 -------------+---------------------------------- F(6, 854) = 2.76 Model | .459843972 6 .076640662 Prob > F = 0.0115 Residual | 23.6942548 854 .027745029 R-squared = 0.0190 -------------+---------------------------------- Adj R-squared = 0.0121 Total | 24.1540988 860 .028086161 Root MSE = .16657 ------------------------------------------------------------------------------ dep | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ind | -.0745096 .0352931 -2.11 0.035 -.1437809 -.0052382 control1 | -.0003018 .0043662 -0.07 0.945 -.0088715 .008268 control2 | -.0133247 .00578 -2.31 0.021 -.0246693 -.00198 control3 | -.0044711 .0044726 -1.00 0.318 -.0132497 .0043075 control4 | .1799002 .0983623 1.83 0.068 -.01316 .3729603 control5 | .0340114 .0191444 1.78 0.076 -.0035642 .0715871 _cons | .7167078 .2593365 2.76 0.006 .2076962 1.22572 ------------------------------------------------------------------------------ Model with mediator regressed on iv Source | SS df MS Number of obs = 861 -------------+---------------------------------- F(6, 854) = 2.90 Model | 3.83093084 6 .638488473 Prob > F = 0.0083 Residual | 187.834574 854 .219946808 R-squared = 0.0200 -------------+---------------------------------- Adj R-squared = 0.0131 Total | 191.665505 860 .222866867 Root MSE = .46898 ------------------------------------------------------------------------------ med | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ind | -.1950457 .0993702 -1.96 0.050 -.3900841 -7.35e-06 control1 | .0279925 .0122933 2.28 0.023 .0038638 .0521211 control2 | .0083398 .016274 0.51 0.608 -.0236019 .0402814 control3 | .0271718 .0125929 2.16 0.031 .0024551 .0518885 control4 | .8851904 .2769459 3.20 0.001 .3416161 1.428765 control5 | .0229235 .0539025 0.43 0.671 -.0828733 .1287204 _cons | 2.094221 .73018 2.87 0.004 .6610633 3.527379 ------------------------------------------------------------------------------ Model with dv regressed on mediator and iv Source | SS df MS Number of obs = 861 -------------+---------------------------------- F(7, 853) = 2.99 Model | .578216371 7 .082602339 Prob > F = 0.0042 Residual | 23.5758824 853 .027638784 R-squared = 0.0239 -------------+---------------------------------- Adj R-squared = 0.0159 Total | 24.1540988 860 .028086161 Root MSE = .16625 ------------------------------------------------------------------------------ dep | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- med | -.0251037 .0121303 -2.07 0.039 -.0489124 -.0012949 ind | -.0794059 .0353048 -2.25 0.025 -.1487004 -.0101114 control1 | .000401 .004371 0.09 0.927 -.0081783 .0089802 control2 | -.0131153 .0057698 -2.27 0.023 -.02444 -.0017906 control3 | -.003789 .0044762 -0.85 0.398 -.0125746 .0049966 control4 | .2021217 .0987592 2.05 0.041 .0082821 .3959613 control5 | .0345869 .0191098 1.81 0.071 -.0029208 .0720946 _cons | .7692805 .2600831 2.96 0.003 .2588026 1.279758 ------------------------------------------------------------------------------ Sobel-Goodman Mediation Tests Coef Std Err Z P>|Z| Sobel .00489637 . . . Goodman-1 .00489637 . . . Goodman-2 .00489637 . . . Pecent of total effect that is mediated: -6.57 % Ratio of indirect to direct effect: -0.0617

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