Mplus model80latent 模型讲解

来自图书《MPlus中介调节模型》

Mplus多重中介潜变量模型速查

• 理论模型
• 数学模型
• 模型公式
• Mplus 代码解读

大家好，欢迎来到Mplus系列教程。今天我们聚焦的是一个包含多个中介变量的潜变量模型。这个模型中，我们有一个潜变量预测变量X，三个潜变量中介变量M1、M2、M3，以及一个潜变量结果变量Y。本教程旨在提供一个速查工具，方便大家在需要使用这类模型时快速参考。本页面列出了本教程的结构：首先我们会介绍模型的理论背景，然后阐述其数学表达方式，接着展示模型的公式，最后我们会详细解读Mplus的代码。希望通过本教程，大家可以快速掌握这类模型的Mplus应用。Data Sense统计咨询团队欢迎您的咨询。

理论模型

我们来看这个模型图。图中圆圈代表变量，箭头表示变量间的关系。X分别影响M1、M2和M3。M1又影响M3和Y，M2也影响M3和Y。M3最后影响Y。同时，X也直接影响Y。

数学模型

这张图展示的是一个多重中介模型，其中X是我们的起点，它会影响三个中间变量，分别是M1，M2和M3。具体来说，X通过a1影响M1，通过a2影响M2，通过a3影响M3。同时，M1会通过b1影响Y，M2会通过b2影响Y，M3会通过b3影响Y。除此之外，M1还会通过d1影响M3，M2会通过d2影响M3，X也会通过c’直接影响Y。所有这些路径共同构成了我们这个复杂的因果关系网络。

数学公式1

模型方程:

Y = b0 + b1M1 + b2M2 + b3M3 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
M3 = a03 + a3X + d1M1 + d2M2

首先，我们从这个包含多个中介变量的模型方程开始。Y是因变量，X是自变量，M1、M2和M3是中介变量。每个变量之间的关系都用不同的系数表示，如b0，b1，a1等。

数学公式2

模型方程:

Y = b0 + b1M1 + b2M2 + b3M3 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
M3 = a03 + a3X + d1M1 + d2M2
第一步：替换 M1 和 M2

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3M3 + c'X
M3 = a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)

接下来，我们将M1和M2的表达式代入到Y和M3的方程中。这里用具体的表达式替换了M1和M2，目的是为了逐步将所有的中介变量用自变量X表示。

数学公式3

模型方程:

Y = b0 + b1M1 + b2M2 + b3M3 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
M3 = a03 + a3X + d1M1 + d2M2
第一步：替换 M1 和 M2

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3M3 + c'X
M3 = a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)
第二步：再次替换 M3

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3(a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)) + c'X

现在，我们将M3的表达式也代入到Y的方程中。 这样，Y的方程就只剩下自变量X和常数项，以及各种系数。这是为了展开分析X对Y的最终影响。

数学公式4

模型方程:

Y = b0 + b1M1 + b2M2 + b3M3 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
M3 = a03 + a3X + d1M1 + d2M2
第一步：替换 M1 和 M2

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3M3 + c'X
M3 = a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)
第二步：再次替换 M3

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3(a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)) + c'X
第三步：展开括号

Y = b0 + a01b1 + a1b1X + a02b2 + a2b2X + a03b3 + a3b3X + a01d1b3 + a1d1b3X + a02d2b3 + a2d2b3X + c'X

现在，我们将Y的方程中的括号全部展开，以便更好地分离各个效应。

数学公式5

模型方程:

Y = b0 + b1M1 + b2M2 + b3M3 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
M3 = a03 + a3X + d1M1 + d2M2
第一步：替换 M1 和 M2

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3M3 + c'X
M3 = a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)
第二步：再次替换 M3

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3(a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)) + c'X
第三步：展开括号

Y = b0 + a01b1 + a1b1X + a02b2 + a2b2X + a03b3 + a3b3X + a01d1b3 + a1d1b3X + a02d2b3 + a2d2b3X + c'X
第四步：整理表达式

Y = (b0 + a01b1 + a02b2 + a03b3 + a01d1b3 + a02d2b3) + (a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3 + c')X

最后，我们将Y的方程整理成截距加斜率的形式，即 Y=a+bX 的形式。括号中的第一部分是截距，表示当X为0时Y的值；第二部分是斜率，表示X对Y的综合影响。

数学公式6

模型方程:

Y = b0 + b1M1 + b2M2 + b3M3 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
M3 = a03 + a3X + d1M1 + d2M2
第一步：替换 M1 和 M2

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3M3 + c'X
M3 = a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)
第二步：再次替换 M3

Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + b3(a03 + a3X + d1(a01 + a1X) + d2(a02 + a2X)) + c'X
第三步：展开括号

Y = b0 + a01b1 + a1b1X + a02b2 + a2b2X + a03b3 + a3b3X + a01d1b3 + a1d1b3X + a02d2b3 + a2d2b3X + c'X
第四步：整理表达式

Y = (b0 + a01b1 + a02b2 + a03b3 + a01d1b3 + a02d2b3) + (a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3 + c')X
效应分解:

间接效应:
* a1b1
* a2b2
* a3b3
* a1b3d1
* a2d2b3

直接效应:
* c'

现在我们得到了X对Y的总效应，并且将其分解为了多个部分。可以看到，X对Y的影响通过了多个中介变量进行传递，这些传递的效应分别由a1b1, a2b2, a3b3, a1b3d1 和 a2d2b3 表示。 而c’则是X对Y的直接效应。

代码解读1

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;

首先，我们定义模型中使用的变量。注释部分解释了每个变量的含义。USEVARIABLES语句列出了所有观测变量，包括：X1到X4，它们用来测量潜在预测变量X；M1_1到M1_4，测量潜在中介变量M1；M2_1到M2_4，测量潜在中介变量M2；M3_1到M3_4，测量潜在中介变量M3；以及Y1到Y4，测量潜在结果变量Y。

代码解读2

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;

接下来，我们设置分析的类型和方法。ANALYSIS部分指定：分析类型为一般分析；使用最大似然估计方法；并使用Bootstrap方法进行参数估计，重复抽样10000次。

代码解读3

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement first state measurement model
! Then state structural model naming each path and intercept using parentheses
MODEL:


! Measurement model

 X BY X1 X2 X3 X4;
 M1 BY M1_1 M1_2 M1_3 M1_4;
 M2 BY M2_1 M2_2 M2_3 M2_4;
 M3 BY M3_1 M3_2 M3_3 M3_4;
 Y BY Y1 Y2 Y3 Y4;

MODEL部分首先定义测量模型。注释说明了MODEL语句的结构。这里，我们定义：潜在变量X由观测变量X1到X4测量；潜在变量M1由M1_1到M1_4测量；潜在变量M2由M2_1到M2_4测量；潜在变量M3由M3_1到M3_4测量；潜在变量Y由Y1到Y4测量。

代码解读4

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement first state measurement model
! Then state structural model naming each path and intercept using parentheses
MODEL:


! Measurement model

 X BY X1 X2 X3 X4;
 M1 BY M1_1 M1_2 M1_3 M1_4;
 M2 BY M2_1 M2_2 M2_3 M2_4;
 M3 BY M3_1 M3_2 M3_3 M3_4;
 Y BY Y1 Y2 Y3 Y4;
! Fit structural model and name parameters

 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON M3 (b3);
 Y ON X (cdash); 
! direct effect of X on Y
 M1 ON X (a1);
 M2 ON X (a2);
 M3 ON X (a3);
 M3 ON M1 (d1);
 M3 ON M2 (d2);

接下来，MODEL部分定义结构模型。我们定义：Y对M1的回归路径，系数命名为b1；Y对M2的回归路径，系数命名为b2；Y对M3的回归路径，系数命名为b3；Y对X的回归路径，即X对Y的直接效应，系数命名为cdash；M1对X的回归路径，系数命名为a1；M2对X的回归路径，系数命名为a2；M3对X的回归路径，系数命名为a3；M3对M1的回归路径，系数命名为d1；以及M3对M2的回归路径，系数命名为d2。

代码解读5

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement first state measurement model
! Then state structural model naming each path and intercept using parentheses
MODEL:


! Measurement model

 X BY X1 X2 X3 X4;
 M1 BY M1_1 M1_2 M1_3 M1_4;
 M2 BY M2_1 M2_2 M2_3 M2_4;
 M3 BY M3_1 M3_2 M3_3 M3_4;
 Y BY Y1 Y2 Y3 Y4;
! Fit structural model and name parameters

 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON M3 (b3);
 Y ON X (cdash); 
! direct effect of X on Y
 M1 ON X (a1);
 M2 ON X (a2);
 M3 ON X (a3);
 M3 ON M1 (d1);
 M3 ON M2 (d2);
! Use model constraint to calculate specific indirect paths and total indirect effect
MODEL CONSTRAINT:
 NEW(a1b1 a2b2 a3b3 a1d1b3 a2d2b3 TOTALIND TOTAL);
 a1b1 = a1*b1; 
! Specific indirect effect of X on Y via M1 only

 a2b2 = a2*b2; 
! Specific indirect effect of X on Y via M2 only

 a3b3 = a3*b3; 
! Specific indirect effect of X on Y via M3 only

 a1d1b3 = a1*d1*b3; 
! Specific indirect effect of X on Y via M1 and M3

 a2d2b3 = a2*d2*b3; 
! Specific indirect effect of X on Y via M2 and M3

 TOTALIND = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3; 
! Total indirect effect of X on Y via M1, M2, M3

 TOTAL = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3 + cdash; 
! Total effect of X on Y

MODEL CONSTRAINT部分使用模型约束计算间接效应和总效应。我们首先创建新的参数来存储计算结果。然后，我们计算：X通过M1对Y的间接效应，存储在a1b1；X通过M2对Y的间接效应，存储在a2b2；X通过M3对Y的间接效应，存储在a3b3；X通过M1和M3对Y的间接效应，存储在a1d1b3；X通过M2和M3对Y的间接效应，存储在a2d2b3；X通过所有中介变量对Y的总间接效应，存储在TOTALIND；以及X对Y的总效应，包括直接效应和所有间接效应，存储在TOTAL。

代码解读6

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement first state measurement model
! Then state structural model naming each path and intercept using parentheses
MODEL:


! Measurement model

 X BY X1 X2 X3 X4;
 M1 BY M1_1 M1_2 M1_3 M1_4;
 M2 BY M2_1 M2_2 M2_3 M2_4;
 M3 BY M3_1 M3_2 M3_3 M3_4;
 Y BY Y1 Y2 Y3 Y4;
! Fit structural model and name parameters

 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON M3 (b3);
 Y ON X (cdash); 
! direct effect of X on Y
 M1 ON X (a1);
 M2 ON X (a2);
 M3 ON X (a3);
 M3 ON M1 (d1);
 M3 ON M2 (d2);
! Use model constraint to calculate specific indirect paths and total indirect effect
MODEL CONSTRAINT:
 NEW(a1b1 a2b2 a3b3 a1d1b3 a2d2b3 TOTALIND TOTAL);
 a1b1 = a1*b1; 
! Specific indirect effect of X on Y via M1 only

 a2b2 = a2*b2; 
! Specific indirect effect of X on Y via M2 only

 a3b3 = a3*b3; 
! Specific indirect effect of X on Y via M3 only

 a1d1b3 = a1*d1*b3; 
! Specific indirect effect of X on Y via M1 and M3

 a2d2b3 = a2*d2*b3; 
! Specific indirect effect of X on Y via M2 and M3

 TOTALIND = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3; 
! Total indirect effect of X on Y via M1, M2, M3

 TOTAL = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3 + cdash; 
! Total effect of X on Y
OUTPUT:

 STAND CINT(bcbootstrap);

OUTPUT部分指定输出选项。我们要求输出标准化系数和Bootstrap置信区间。

代码解读7

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement first state measurement model
! Then state structural model naming each path and intercept using parentheses
MODEL:


! Measurement model

 X BY X1 X2 X3 X4;
 M1 BY M1_1 M1_2 M1_3 M1_4;
 M2 BY M2_1 M2_2 M2_3 M2_4;
 M3 BY M3_1 M3_2 M3_3 M3_4;
 Y BY Y1 Y2 Y3 Y4;
! Fit structural model and name parameters

 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON M3 (b3);
 Y ON X (cdash); 
! direct effect of X on Y
 M1 ON X (a1);
 M2 ON X (a2);
 M3 ON X (a3);
 M3 ON M1 (d1);
 M3 ON M2 (d2);
! Use model constraint to calculate specific indirect paths and total indirect effect
MODEL CONSTRAINT:
 NEW(a1b1 a2b2 a3b3 a1d1b3 a2d2b3 TOTALIND TOTAL);
 a1b1 = a1*b1; 
! Specific indirect effect of X on Y via M1 only

 a2b2 = a2*b2; 
! Specific indirect effect of X on Y via M2 only

 a3b3 = a3*b3; 
! Specific indirect effect of X on Y via M3 only

 a1d1b3 = a1*d1*b3; 
! Specific indirect effect of X on Y via M1 and M3

 a2d2b3 = a2*d2*b3; 
! Specific indirect effect of X on Y via M2 and M3

 TOTALIND = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3; 
! Total indirect effect of X on Y via M1, M2, M3

 TOTAL = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3 + cdash; 
! Total effect of X on Y
OUTPUT:

 STAND CINT(bcbootstrap);
MODEL:

 Y ON X M1 M2 M3;
 M1 M2 ON X;
 M3 ON M1 M2 X;

这是使用MODEL INDIRECT替代MODEL CONSTRAINT时,MODEL部分的结构模型定义. 我们定义: Y对X, M1, M2, M3的回归路径; M1和M2对X的回归路径; M3对M1, M2, 和X的回归路径。注意这里没有指定系数名称

代码解读8

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement first state measurement model
! Then state structural model naming each path and intercept using parentheses
MODEL:


! Measurement model

 X BY X1 X2 X3 X4;
 M1 BY M1_1 M1_2 M1_3 M1_4;
 M2 BY M2_1 M2_2 M2_3 M2_4;
 M3 BY M3_1 M3_2 M3_3 M3_4;
 Y BY Y1 Y2 Y3 Y4;
! Fit structural model and name parameters

 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON M3 (b3);
 Y ON X (cdash); 
! direct effect of X on Y
 M1 ON X (a1);
 M2 ON X (a2);
 M3 ON X (a3);
 M3 ON M1 (d1);
 M3 ON M2 (d2);
! Use model constraint to calculate specific indirect paths and total indirect effect
MODEL CONSTRAINT:
 NEW(a1b1 a2b2 a3b3 a1d1b3 a2d2b3 TOTALIND TOTAL);
 a1b1 = a1*b1; 
! Specific indirect effect of X on Y via M1 only

 a2b2 = a2*b2; 
! Specific indirect effect of X on Y via M2 only

 a3b3 = a3*b3; 
! Specific indirect effect of X on Y via M3 only

 a1d1b3 = a1*d1*b3; 
! Specific indirect effect of X on Y via M1 and M3

 a2d2b3 = a2*d2*b3; 
! Specific indirect effect of X on Y via M2 and M3

 TOTALIND = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3; 
! Total indirect effect of X on Y via M1, M2, M3

 TOTAL = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3 + cdash; 
! Total effect of X on Y
OUTPUT:

 STAND CINT(bcbootstrap);
MODEL:

 Y ON X M1 M2 M3;
 M1 M2 ON X;
 M3 ON M1 M2 X;
MODEL INDIRECT:
 Y IND X;

这里我们使用MODEL INDIRECT子命令来计算间接效应。Y IND X表示计算X对Y的间接效应。

代码解读9

! Latent predictor variable X measured by X1-X4
! Latent mediator variables M1, M2 and M3, measured by M1_1-M1_4, M2_1-M2_4 and M3_1-M3_4 respectively
! Moderator variable(s) - none
! Latent outcome variable Y measured by Y1-Y4
USEVARIABLES = X1 X2 X3 X4 M1_1 M1_2 M1_3 M1_4 M2_1 M2_2 M2_3 M2_4 M3_1 M3_2 M3_3 M3_4 Y1 Y2 Y3 Y4;
ANALYSIS:

 TYPE = GENERAL;
 ESTIMATOR = ML;
 BOOTSTRAP = 10000;
! In model statement first state measurement model
! Then state structural model naming each path and intercept using parentheses
MODEL:


! Measurement model

 X BY X1 X2 X3 X4;
 M1 BY M1_1 M1_2 M1_3 M1_4;
 M2 BY M2_1 M2_2 M2_3 M2_4;
 M3 BY M3_1 M3_2 M3_3 M3_4;
 Y BY Y1 Y2 Y3 Y4;
! Fit structural model and name parameters

 Y ON M1 (b1);
 Y ON M2 (b2);
 Y ON M3 (b3);
 Y ON X (cdash); 
! direct effect of X on Y
 M1 ON X (a1);
 M2 ON X (a2);
 M3 ON X (a3);
 M3 ON M1 (d1);
 M3 ON M2 (d2);
! Use model constraint to calculate specific indirect paths and total indirect effect
MODEL CONSTRAINT:
 NEW(a1b1 a2b2 a3b3 a1d1b3 a2d2b3 TOTALIND TOTAL);
 a1b1 = a1*b1; 
! Specific indirect effect of X on Y via M1 only

 a2b2 = a2*b2; 
! Specific indirect effect of X on Y via M2 only

 a3b3 = a3*b3; 
! Specific indirect effect of X on Y via M3 only

 a1d1b3 = a1*d1*b3; 
! Specific indirect effect of X on Y via M1 and M3

 a2d2b3 = a2*d2*b3; 
! Specific indirect effect of X on Y via M2 and M3

 TOTALIND = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3; 
! Total indirect effect of X on Y via M1, M2, M3

 TOTAL = a1b1 + a2b2 + a3b3 + a1d1b3 + a2d2b3 + cdash; 
! Total effect of X on Y
OUTPUT:

 STAND CINT(bcbootstrap);
MODEL:

 Y ON X M1 M2 M3;
 M1 M2 ON X;
 M3 ON M1 M2 X;
MODEL INDIRECT:
 Y IND X;
OUTPUT:

 STAND CINT(bcbootstrap);

OUTPUT部分与之前MODEL CONSTRAINT版本的设置相同。我们仍然要求输出标准化系数和Bootstrap置信区间。

资源汇总

• 本视频讲义地址: https://mlln.cn/mplus-model-templates/model80latent.html
• 图书《MPlus中介调节模型》打包下载: 点击下载
• 图书《MPlus中介调节模型》在线看: 点击查看
• 视频教程: 点击这里打开视频
• Mplus 模型模板教程列表: https://mlln.cn/mplus-model-templates
• 统计咨询: https://wx.zsxq.com/group/88888188828842

感谢大家的收看！ 本节课的资源已全部提供，请大家点击页面上的链接下载或查看。 希望这份资料对大家的学习有所帮助，也欢迎大家点赞收藏，方便日后查阅！