Mplus model83 模型讲解

来自图书《MPlus中介调节模型》

Mplus 复杂调节中介模型速查教程

• 理论模型
• 数学模型
• 数学推导
• 代码解读

大家好，欢迎来到Data Sense统计咨询团队的Mplus复杂调节中介模型速查教程。本教程旨在帮助大家快速掌握Mplus中复杂调节中介模型的建模和分析。我们主要关注实用性，希望大家在遇到类似模型时能够快速上手。本教程将围绕以下四个方面展开：首先，我们会介绍模型的理论基础，让大家理解模型背后的概念。接着，我们会详细展示模型的数学表达形式，以及如何推导出模型中的各个路径系数。然后，我们会一步步解读Mplus代码，说明如何将理论模型转化为可执行的Mplus指令。最后，我们还会教大家如何解读Mplus的输出结果，理解模型参数的含义。希望通过本教程，大家能够对Mplus中复杂的调节中介模型有更加清晰和深入的理解。如果大家在学习过程中有任何疑问，欢迎随时联系我们Data Sense团队进行咨询。

理论模型

我们来看这个模型图，它展示了五个变量之间的关系。W影响M1和X，M1影响M2和Y，X也影响M1和Y，M2则影响Y。这些箭头表示变量之间的影响关系。

数学模型

这张图展示了我们研究的数学模型。图中，长方形代表变量，箭头表示变量之间的影响关系。XW、W和X这三个变量都影响着M1；同时，X还直接影响M2和Y。M1除了影响Y，还通过M2间接影响Y。箭头旁的字母代表路径系数，例如a1表示X对M1的影响，a2表示X对Y的影响，b1表示M1对M2的影响，b2表示M2对Y的影响，c’ 表示X对Y的直接影响，d1表示M1对M2的影响。这个模型展示了不同变量之间复杂的相互作用关系。

数学公式1

模型方程:

Y = b_0 + b_1M_1 + b_2M_2 + c'X
M_1 = a_{01} + a_1X + a_3W + a_4XW
M_2 = a_{02} + a_2X + d_1M_1

首先，我们来看模型方程。Y是因变量，X是自变量，M1和M2是中介变量，W是调节变量。这些方程描述了它们之间的关系。

数学公式2

模型方程:

Y = b_0 + b_1M_1 + b_2M_2 + c'X
M_1 = a_{01} + a_1X + a_3W + a_4XW
M_2 = a_{02} + a_2X + d_1M_1
代入M_1:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2M_2 + c'X

接下来，我们将M1的表达式代入Y的方程中。

数学公式3

模型方程:

Y = b_0 + b_1M_1 + b_2M_2 + c'X
M_1 = a_{01} + a_1X + a_3W + a_4XW
M_2 = a_{02} + a_2X + d_1M_1
代入M_1:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2M_2 + c'X
代入M_2:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2(a_{02} + a_2X + d_1(a_{01} + a_1X + a_3W + a_4XW)) + c'X

然后，我们将M2的表达式也代入Y的方程中。

数学公式4

模型方程:

Y = b_0 + b_1M_1 + b_2M_2 + c'X
M_1 = a_{01} + a_1X + a_3W + a_4XW
M_2 = a_{02} + a_2X + d_1M_1
代入M_1:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2M_2 + c'X
代入M_2:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2(a_{02} + a_2X + d_1(a_{01} + a_1X + a_3W + a_4XW)) + c'X
展开括号:

Y = b_0 + a_{01}b_1 + a_1b_1X + a_3b_1W + a_4b_1XW + a_{02}b_2 + a_2b_2X + a_{01}d_1b_2 + a_1d_1b_2X + a_3d_1b_2W + a_4d_1b_2XW + c'X

现在，我们展开所有的括号。

数学公式5

模型方程:

Y = b_0 + b_1M_1 + b_2M_2 + c'X
M_1 = a_{01} + a_1X + a_3W + a_4XW
M_2 = a_{02} + a_2X + d_1M_1
代入M_1:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2M_2 + c'X
代入M_2:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2(a_{02} + a_2X + d_1(a_{01} + a_1X + a_3W + a_4XW)) + c'X
展开括号:

Y = b_0 + a_{01}b_1 + a_1b_1X + a_3b_1W + a_4b_1XW + a_{02}b_2 + a_2b_2X + a_{01}d_1b_2 + a_1d_1b_2X + a_3d_1b_2W + a_4d_1b_2XW + c'X
分组整理:

Y = (b_0 + a_{01}b_1 + a_{02}b_2 + a_{01}d_1b_2 + a_3b_1W + a_3d_1b_2W) + (a_1b_1 + a_2b_2 + a_1d_1b_2 + a_4b_1W + a_4d_1b_2W + c')X

接下来，我们将方程按照是否含有X进行分组，整理成Y=a+bX的形式。

数学公式6

模型方程:

Y = b_0 + b_1M_1 + b_2M_2 + c'X
M_1 = a_{01} + a_1X + a_3W + a_4XW
M_2 = a_{02} + a_2X + d_1M_1
代入M_1:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2M_2 + c'X
代入M_2:

Y = b_0 + b_1(a_{01} + a_1X + a_3W + a_4XW) + b_2(a_{02} + a_2X + d_1(a_{01} + a_1X + a_3W + a_4XW)) + c'X
展开括号:

Y = b_0 + a_{01}b_1 + a_1b_1X + a_3b_1W + a_4b_1XW + a_{02}b_2 + a_2b_2X + a_{01}d_1b_2 + a_1d_1b_2X + a_3d_1b_2W + a_4d_1b_2XW + c'X
分组整理:

Y = (b_0 + a_{01}b_1 + a_{02}b_2 + a_{01}d_1b_2 + a_3b_1W + a_3d_1b_2W) + (a_1b_1 + a_2b_2 + a_1d_1b_2 + a_4b_1W + a_4d_1b_2W + c')X
效应:

有调节的间接效应:
(a_1 + a_4W)b_1
a_2b_2
(a_1 + a_4W)d_1b_2

直接效应:
c'

最后，通过整理后的方程，我们得到三个有调节的间接效应和一个直接效应。这些效应描述了X如何通过中介变量或者直接影响Y。其中，间接效应还受到了调节变量W的影响。

代码解读1

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;

第一步，我们先来看这段代码的开头。以感叹号开头的行是注释，用来解释代码的。这里定义了变量的角色：X是预测变量，M1和M2是中介变量，W是调节变量，Y是结果变量。USEVARIABLES这行则声明了分析中用到的所有变量，包括预测变量X，中介变量M1和M2，调节变量W，结果变量Y，以及交互项XW。注意XW是X和W的交互项，需要预先计算好。

代码解读2

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;

第二步，这里是定义交互项的部分。DEFINE语句告诉Mplus我们要创建一个新的变量。XW = X*W 这行代码的意思是，变量XW是变量X和变量W的乘积，也就是我们所说的交互项。

代码解读3

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;

第三步，ANALYSIS部分开始定义分析的设置。TYPE = GENERAL 说明这是一个一般的模型分析。ESTIMATOR = ML 指定使用最大似然估计方法。BOOTSTRAP = 10000 表示进行10000次bootstrap抽样，用来计算置信区间。

代码解读4

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);

第四步，MODEL部分开始定义模型结构。这里我们给每个路径都起了名字，用括号括起来。Y ON M1 (b1)表示Y对M1的回归路径，系数命名为b1。同理，Y ON M2 (b2)表示Y对M2的回归路径，系数为b2。 Y ON X (cdash)表示Y对X的直接效应，系数是cdash，也就是c’。

代码解读5

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);

第五步，继续定义模型路径。M1 ON X (a1)表示M1对X的回归路径，系数为a1。M1 ON W (a3)表示M1对W的回归路径，系数为a3。M1 ON XW (a4)表示M1对交互项XW的回归路径，系数为a4。M2 ON X (a2)表示M2对X的回归路径，系数为a2。M2 ON M1 (d1)表示M2对M1的回归路径，系数为d1。

代码解读6

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,

! for example, of 1 SD below mean, mean, 1 SD above mean

! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
 NEW(LOW_W MED_W HIGH_W a2b2
 LWa1b1 MWa1b1 HWa1b1
 LWa1d1b2 MWa1d1b2 HWa1d1b2
 IMM_A IMM_B
 TOT_LOWW TOT_MEDW TOT_HIW);

第六步，MODEL CONSTRAINT 部分用来计算各种效应和检验简单斜率。这部分首先声明了很多新的变量，用于存储计算结果，比如LOW_W，MED_W，HIGH_W是调节变量W的低、中、高值，其他的则是各种效应值。

代码解读7

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,

! for example, of 1 SD below mean, mean, 1 SD above mean

! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
 NEW(LOW_W MED_W HIGH_W a2b2
 LWa1b1 MWa1b1 HWa1b1
 LWa1d1b2 MWa1d1b2 HWa1d1b2
 IMM_A IMM_B
 TOT_LOWW TOT_MEDW TOT_HIW);
 LOW_W = #LOWW;
! replace #LOWW in the code with your chosen low value of W

 MED_W = #MEDW;
! replace #MEDW in the code with your chosen medium value of W

 HIGH_W = #HIGHW;
! replace #HIGHW in the code with your chosen high value of W

第七步，这里定义调节变量W的低、中、高值。LOW_W = #LOWW 表示将变量LOW_W设定为用户选择的W的低值。代码注释提示我们需要用实际的数值替换#LOWW，同理，MED_W和HIGH_W也需要替换为W的中值和高值。

代码解读8

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,

! for example, of 1 SD below mean, mean, 1 SD above mean

! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
 NEW(LOW_W MED_W HIGH_W a2b2
 LWa1b1 MWa1b1 HWa1b1
 LWa1d1b2 MWa1d1b2 HWa1d1b2
 IMM_A IMM_B
 TOT_LOWW TOT_MEDW TOT_HIW);
 LOW_W = #LOWW;
! replace #LOWW in the code with your chosen low value of W

 MED_W = #MEDW;
! replace #MEDW in the code with your chosen medium value of W

 HIGH_W = #HIGHW;
! replace #HIGHW in the code with your chosen high value of W
! Now calc indirect and total effects for each value of W
! Conditional indirect effects of X on Y via M1 only given values of W
 LWa1b1 = a1*b1 + a4*b1*LOW_W;
 MWa1b1 = a1*b1 + a4*b1*MED_W;
 HWa1b1 = a1*b1 + a4*b1*HIGH_W;
 a2b2 = a2*b2;

第八步，开始计算不同W值下的间接效应。LWa1b1计算的是当W取低值时，X通过M1对Y的间接效应。MWa1b1和HWa1b1分别计算W取中值和高值时的间接效应。a2b2计算X通过M2对Y的间接效应。

代码解读9

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,

! for example, of 1 SD below mean, mean, 1 SD above mean

! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
 NEW(LOW_W MED_W HIGH_W a2b2
 LWa1b1 MWa1b1 HWa1b1
 LWa1d1b2 MWa1d1b2 HWa1d1b2
 IMM_A IMM_B
 TOT_LOWW TOT_MEDW TOT_HIW);
 LOW_W = #LOWW;
! replace #LOWW in the code with your chosen low value of W

 MED_W = #MEDW;
! replace #MEDW in the code with your chosen medium value of W

 HIGH_W = #HIGHW;
! replace #HIGHW in the code with your chosen high value of W
! Now calc indirect and total effects for each value of W
! Conditional indirect effects of X on Y via M1 only given values of W
 LWa1b1 = a1*b1 + a4*b1*LOW_W;
 MWa1b1 = a1*b1 + a4*b1*MED_W;
 HWa1b1 = a1*b1 + a4*b1*HIGH_W;
 a2b2 = a2*b2;
! Specific indirect effect of X on Y via M2 only
! Conditional indirect effects of X on Y via M1 and M2 given values of W
 LWa1d1b2 = a1*d1*b2 + a4*d1*b2*LOW_W;
 MWa1d1b2 = a1*d1*b2 + a4*d1*b2*MED_W;
 HWa1d1b2 = a1*d1*b2 + a4*d1*b2*HIGH_W;

第九步，这里计算X通过M1和M2对Y的条件间接效应。LWa1d1b2计算当W取低值时的间接效应，MWa1d1b2和HWa1d1b2分别计算W取中值和高值时的间接效应。

代码解读10

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,

! for example, of 1 SD below mean, mean, 1 SD above mean

! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
 NEW(LOW_W MED_W HIGH_W a2b2
 LWa1b1 MWa1b1 HWa1b1
 LWa1d1b2 MWa1d1b2 HWa1d1b2
 IMM_A IMM_B
 TOT_LOWW TOT_MEDW TOT_HIW);
 LOW_W = #LOWW;
! replace #LOWW in the code with your chosen low value of W

 MED_W = #MEDW;
! replace #MEDW in the code with your chosen medium value of W

 HIGH_W = #HIGHW;
! replace #HIGHW in the code with your chosen high value of W
! Now calc indirect and total effects for each value of W
! Conditional indirect effects of X on Y via M1 only given values of W
 LWa1b1 = a1*b1 + a4*b1*LOW_W;
 MWa1b1 = a1*b1 + a4*b1*MED_W;
 HWa1b1 = a1*b1 + a4*b1*HIGH_W;
 a2b2 = a2*b2;
! Specific indirect effect of X on Y via M2 only
! Conditional indirect effects of X on Y via M1 and M2 given values of W
 LWa1d1b2 = a1*d1*b2 + a4*d1*b2*LOW_W;
 MWa1d1b2 = a1*d1*b2 + a4*d1*b2*MED_W;
 HWa1d1b2 = a1*d1*b2 + a4*d1*b2*HIGH_W;
! Indices of Moderated Mediation
 IMM_A = a4*b1;
 IMM_B = a4*d1*b2;
! Conditional total effects of X on Y given values of W
 TOT_LOWW = LWa1d1b2 + LWa1b1 + a2b2 + cdash;
 TOT_MEDW = MWa1d1b2 + MWa1b1 + a2b2 + cdash;
 TOT_HIW = HWa1d1b2 + HWa1b1 + a2b2 + cdash;

第十步，计算调节中介的指数和总效应。IMM_A计算X通过M1对Y的调节中介指数，IMM_B计算X通过M1和M2对Y的调节中介指数。TOT_LOWW，TOT_MEDW和TOT_HIW分别计算当W取低、中、高值时，X对Y的总效应。

代码解读11

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,

! for example, of 1 SD below mean, mean, 1 SD above mean

! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
 NEW(LOW_W MED_W HIGH_W a2b2
 LWa1b1 MWa1b1 HWa1b1
 LWa1d1b2 MWa1d1b2 HWa1d1b2
 IMM_A IMM_B
 TOT_LOWW TOT_MEDW TOT_HIW);
 LOW_W = #LOWW;
! replace #LOWW in the code with your chosen low value of W

 MED_W = #MEDW;
! replace #MEDW in the code with your chosen medium value of W

 HIGH_W = #HIGHW;
! replace #HIGHW in the code with your chosen high value of W
! Now calc indirect and total effects for each value of W
! Conditional indirect effects of X on Y via M1 only given values of W
 LWa1b1 = a1*b1 + a4*b1*LOW_W;
 MWa1b1 = a1*b1 + a4*b1*MED_W;
 HWa1b1 = a1*b1 + a4*b1*HIGH_W;
 a2b2 = a2*b2;
! Specific indirect effect of X on Y via M2 only
! Conditional indirect effects of X on Y via M1 and M2 given values of W
 LWa1d1b2 = a1*d1*b2 + a4*d1*b2*LOW_W;
 MWa1d1b2 = a1*d1*b2 + a4*d1*b2*MED_W;
 HWa1d1b2 = a1*d1*b2 + a4*d1*b2*HIGH_W;
! Indices of Moderated Mediation
 IMM_A = a4*b1;
 IMM_B = a4*d1*b2;
! Conditional total effects of X on Y given values of W
 TOT_LOWW = LWa1d1b2 + LWa1b1 + a2b2 + cdash;
 TOT_MEDW = MWa1d1b2 + MWa1b1 + a2b2 + cdash;
 TOT_HIW = HWa1d1b2 + HWa1b1 + a2b2 + cdash;
! Use loop plot to plot total effect of X on Y for low, med, high values of W

! NOTE - values of 1,5 in LOOP() statement need to be replaced by

! logical min and max limits of predictor X used in analysis
 PLOT(LOMOD MEDMOD HIMOD);
 LOOP(XVAL,1,5,0.1);
 LOMOD = TOT_LOWW*XVAL;
 MEDMOD = TOT_MEDW*XVAL;
 HIMOD = TOT_HIW*XVAL;

第十一步，准备绘制总效应图。PLOT(LOMOD MEDMOD HIMOD)声明要绘制的变量。LOOP(XVAL,1,5,0.1)创建一个循环，让XVAL从1到5，步长为0.1。代码注释提醒我们需要用分析中预测变量X的实际最小值和最大值替换1和5。LOMOD，MEDMOD和HIMOD分别计算当W取低、中、高值时，不同X值下的Y的预测值。

代码解读12

! Predictor variable - X
! Mediator variable(s) – M1, M2
! Moderator variable(s) - W
! Outcome variable - Y
USEVARIABLES = X M1 M2 W Y XW;
! Create interaction term
DEFINE:
 XW = X*W;
ANALYSIS:
 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON X (cdash);
! direct effect of X on Y
 M1 ON X (a1);
 M1 ON W (a3);
 M1 ON XW (a4);
 M2 ON X (a2);
 M2 ON M1 (d1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,

! for example, of 1 SD below mean, mean, 1 SD above mean

! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
 NEW(LOW_W MED_W HIGH_W a2b2
 LWa1b1 MWa1b1 HWa1b1
 LWa1d1b2 MWa1d1b2 HWa1d1b2
 IMM_A IMM_B
 TOT_LOWW TOT_MEDW TOT_HIW);
 LOW_W = #LOWW;
! replace #LOWW in the code with your chosen low value of W

 MED_W = #MEDW;
! replace #MEDW in the code with your chosen medium value of W

 HIGH_W = #HIGHW;
! replace #HIGHW in the code with your chosen high value of W
! Now calc indirect and total effects for each value of W
! Conditional indirect effects of X on Y via M1 only given values of W
 LWa1b1 = a1*b1 + a4*b1*LOW_W;
 MWa1b1 = a1*b1 + a4*b1*MED_W;
 HWa1b1 = a1*b1 + a4*b1*HIGH_W;
 a2b2 = a2*b2;
! Specific indirect effect of X on Y via M2 only
! Conditional indirect effects of X on Y via M1 and M2 given values of W
 LWa1d1b2 = a1*d1*b2 + a4*d1*b2*LOW_W;
 MWa1d1b2 = a1*d1*b2 + a4*d1*b2*MED_W;
 HWa1d1b2 = a1*d1*b2 + a4*d1*b2*HIGH_W;
! Indices of Moderated Mediation
 IMM_A = a4*b1;
 IMM_B = a4*d1*b2;
! Conditional total effects of X on Y given values of W
 TOT_LOWW = LWa1d1b2 + LWa1b1 + a2b2 + cdash;
 TOT_MEDW = MWa1d1b2 + MWa1b1 + a2b2 + cdash;
 TOT_HIW = HWa1d1b2 + HWa1b1 + a2b2 + cdash;
! Use loop plot to plot total effect of X on Y for low, med, high values of W

! NOTE - values of 1,5 in LOOP() statement need to be replaced by

! logical min and max limits of predictor X used in analysis
 PLOT(LOMOD MEDMOD HIMOD);
 LOOP(XVAL,1,5,0.1);
 LOMOD = TOT_LOWW*XVAL;
 MEDMOD = TOT_MEDW*XVAL;
 HIMOD = TOT_HIW*XVAL;
PLOT:
 TYPE = plot2;
OUTPUT:
 STAND CINT(bcbootstrap);

第十二步，最后定义绘图类型和输出。PLOT: TYPE = plot2 指定绘制类型为散点图或线图。OUTPUT: STAND CINT(bcbootstrap)指定输出标准化系数和置信区间，置信区间通过bootstrap方法计算。这样就完成了整个Mplus代码的解读。

资源汇总

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