Mplus model38latent 模型讲解

来自图书《MPlus中介调节模型》

Mplus复杂模型构建：包含中介、调节及中介调节效应

• 理论模型
• 数学模型
• 数学推导
• 代码解读

大家好，欢迎来到本教程。 今天我们将学习如何使用Mplus构建一个非常复杂的模型，它包含中介效应、调节效应以及中介调节效应。 这个模型非常强大，能够分析多个变量之间错综复杂的关系。

本教程将循序渐进地讲解这个模型的构建过程。首先，我们会介绍模型的理论基础，也就是我们希望研究什么变量之间的关系，以及这些关系之间是如何相互作用的。 这部分内容会帮助大家理解模型的逻辑框架。

接下来，我们将建立模型的数学表达式，用数学公式来清晰地表达变量之间的关系以及它们的相互作用，例如中介效应和调节效应是如何用公式表示的。

然后，我们会详细讲解数学推导过程，一步步地展现模型是如何从理论假设转化为可以用于Mplus分析的数学模型的。

最后，也是最重要的一部分，我们将详细解读Mplus的代码，并解释每一行代码的含义和作用，帮助大家理解如何将复杂的数学模型转化为Mplus可以运行的代码。

本教程旨在帮助大家快速上手，所以内容安排比较精简，大家可以根据自己的需求选择性地学习。 希望这个教程能够帮助大家更好地理解和应用Mplus进行复杂模型的构建。如有任何问题，欢迎随时提出。

理论模型

我们来看，这张图展示了一个比较复杂的模型。这里有X、M和Y三个主要变量。箭头表示它们之间的关系。X可以直接影响M，同时M又会影响Y，当然X本身也会直接影响Y。同时，还有W、V和Q三个变量会起到调节作用。其中，W会影响X对M的影响；V和Q则会影响M对Y的影响，并且V和Q之间本身也有相互作用。更有意思的是，V和Q还可以调节X对Y的影响，它们之间的交互作用也可以调节X对Y的影响。最后，M、V和Q的三者共同作用，以及X、V和Q的三者共同作用，都会对Y产生影响。这个模型研究了X如何通过M影响Y，以及W、V和Q如何影响这些关系。

数学模型

这张图展示了一个复杂的模型，它研究的是变量之间的相互影响。其中，X是一个起始变量，它会通过a1影响中间变量M。同时，W也会通过a2影响M。此外，X和W的相互作用，也就是XW，也会通过a3影响M。中间变量M会通过b1影响最终结果Y。

除了直接影响，M还会跟其他变量产生互动，比如M和V的互动MV通过b2影响Y；M和Q的互动MQ通过b3影响Y；M、V和Q三者共同的互动MVQ通过b4影响Y。

另外，X还会通过c1’直接影响Y，V通过c2’直接影响Y，Q通过c3’直接影响Y。变量之间也有更复杂的互动，比如X和V的互动XV通过c4’影响Y；X和Q的互动XQ通过c5’影响Y；V和Q的互动VQ通过c6’影响Y；以及X、V、Q三者的互动XVQ通过c7’影响Y。

总的来说，这个模型考虑了X如何通过M影响Y，以及W、V、Q这些变量是如何调节这些影响的，并且还包含了各种变量之间复杂的互动关系。

数学公式1

模型方程式:

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

M = a0 + a1X + a2W + a3XW

首先，我们来看一下模型方程式。Y是因变量，M是中介变量，X是自变量，W、V和Q是调节变量。第一个方程式描述了Y如何受到M、X以及它们之间交互作用的影响。第二个方程式描述了M如何受到X和W以及它们之间交互作用的影响。

数学公式2

模型方程式:

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

M = a0 + a1X + a2W + a3XW
将M的方程式代入Y的方程式:

Y = b0 + b1(a0 + a1X + a2W + a3XW) + b2(a0 + a1X + a2W + a3XW)V + b3(a0 + a1X + a2W + a3XW)Q + b4(a0 + a1X + a2W + a3XW)VQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

接下来，我们将M的方程式代入到Y的方程式中，这样我们就可以直接看到X是如何通过M影响Y的。这个步骤主要是为了展开分析X对Y的总效应。

数学公式3

模型方程式:

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

M = a0 + a1X + a2W + a3XW
将M的方程式代入Y的方程式:

Y = b0 + b1(a0 + a1X + a2W + a3XW) + b2(a0 + a1X + a2W + a3XW)V + b3(a0 + a1X + a2W + a3XW)Q + b4(a0 + a1X + a2W + a3XW)VQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ
展开括号并合并同类项:

Y = b0 + a0b1 + a1b1X + a2b1W + a3b1XW + a0b2V + a1b2XV + a2b2WV + a3b2XWV + a0b3Q + a1b3XQ + a2b3WQ + a3b3XWQ + a0b4VQ + a1b4XVQ + a2b4WVQ + a3b4XWVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

现在，我们展开括号并合并所有包含相同变量的项。这个步骤旨在整理公式，使其更容易区分直接效应和间接效应。

数学公式4

模型方程式:

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

M = a0 + a1X + a2W + a3XW
将M的方程式代入Y的方程式:

Y = b0 + b1(a0 + a1X + a2W + a3XW) + b2(a0 + a1X + a2W + a3XW)V + b3(a0 + a1X + a2W + a3XW)Q + b4(a0 + a1X + a2W + a3XW)VQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ
展开括号并合并同类项:

Y = b0 + a0b1 + a1b1X + a2b1W + a3b1XW + a0b2V + a1b2XV + a2b2WV + a3b2XWV + a0b3Q + a1b3XQ + a2b3WQ + a3b3XWQ + a0b4VQ + a1b4XVQ + a2b4WVQ + a3b4XWVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ
整理成 Y = a + bX 的形式:

Y = (b0 + a0b1 + a2b1W + a0b2V + a2b2WV + a0b3Q + a2b3WQ + a0b4VQ + a2b4WVQ + c2'V + c3'Q + c6'VQ) + (a1b1 + a3b1W + a1b2V + a3b2WV + a1b3Q + a3b3WQ + a1b4VQ + a3b4WVQ + c1' + c4'V + c5'Q + c7'VQ)X

为了更清晰地展示X对Y的影响，我们将公式整理成Y = a + bX的形式，其中a部分表示截距，b部分表示X对Y的效应。这样可以将X的直接效应和间接效应明确分离。

数学公式5

模型方程式:

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

M = a0 + a1X + a2W + a3XW
将M的方程式代入Y的方程式:

Y = b0 + b1(a0 + a1X + a2W + a3XW) + b2(a0 + a1X + a2W + a3XW)V + b3(a0 + a1X + a2W + a3XW)Q + b4(a0 + a1X + a2W + a3XW)VQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ
展开括号并合并同类项:

Y = b0 + a0b1 + a1b1X + a2b1W + a3b1XW + a0b2V + a1b2XV + a2b2WV + a3b2XWV + a0b3Q + a1b3XQ + a2b3WQ + a3b3XWQ + a0b4VQ + a1b4XVQ + a2b4WVQ + a3b4XWVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ
整理成 Y = a + bX 的形式:

Y = (b0 + a0b1 + a2b1W + a0b2V + a2b2WV + a0b3Q + a2b3WQ + a0b4VQ + a2b4WVQ + c2'V + c3'Q + c6'VQ) + (a1b1 + a3b1W + a1b2V + a3b2WV + a1b3Q + a3b3WQ + a1b4VQ + a3b4WVQ + c1' + c4'V + c5'Q + c7'VQ)X
X对Y的间接效应 (在W, V, Q条件下):

(a1 + a3W)(b1 + b2V + b3Q + b4VQ)

通过整理后的公式，我们可以清晰地看到X对Y的间接效应，即通过中介变量M影响Y的部分。这部分效应是由（a1 + a3W）和（b1 + b2V + b3Q + b4VQ）的乘积决定的，它包含了调节变量W,V,Q的影响。

数学公式6

模型方程式:

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ

M = a0 + a1X + a2W + a3XW
将M的方程式代入Y的方程式:

Y = b0 + b1(a0 + a1X + a2W + a3XW) + b2(a0 + a1X + a2W + a3XW)V + b3(a0 + a1X + a2W + a3XW)Q + b4(a0 + a1X + a2W + a3XW)VQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ
展开括号并合并同类项:

Y = b0 + a0b1 + a1b1X + a2b1W + a3b1XW + a0b2V + a1b2XV + a2b2WV + a3b2XWV + a0b3Q + a1b3XQ + a2b3WQ + a3b3XWQ + a0b4VQ + a1b4XVQ + a2b4WVQ + a3b4XWVQ + c1'X + c2'V + c3'Q + c4'XV + c5'XQ + c6'VQ + c7'XVQ
整理成 Y = a + bX 的形式:

Y = (b0 + a0b1 + a2b1W + a0b2V + a2b2WV + a0b3Q + a2b3WQ + a0b4VQ + a2b4WVQ + c2'V + c3'Q + c6'VQ) + (a1b1 + a3b1W + a1b2V + a3b2WV + a1b3Q + a3b3WQ + a1b4VQ + a3b4WVQ + c1' + c4'V + c5'Q + c7'VQ)X
X对Y的间接效应 (在W, V, Q条件下):

(a1 + a3W)(b1 + b2V + b3Q + b4VQ)
X对Y的直接效应 (在V, Q条件下):

c1' + c4'V + c5'Q + c7'VQ

最后，我们得到了X对Y的直接效应，即不通过中介变量M，X直接影响Y的部分。这部分效应是由c1’加上c4’V、c5’Q和c7’VQ决定的，它受到调节变量V和Q的影响。

代码解读1

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;

第一步，我们定义分析中要用到的变量。X1到X4是用来测量潜在自变量X的，M1到M4是用来测量潜在中介变量M的。W1到W4、V1到V4、Q1到Q4分别用来测量三个调节变量W、V和Q。最后，Y1到Y4用来测量因变量Y。

代码解读2

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;

第二步，设置分析参数。我们使用一般随机效应模型，采用极大似然估计方法，并使用数值积分算法来处理非线性项。

代码解读3

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;

第三步，定义测量模型。这里我们将潜在变量与它们的测量指标联系起来。例如，X由X1到X4测量，M由M1到M4测量。W、V和Q的方差都固定为1，并标准化。

代码解读4

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;

第四步，我们将调节变量W、V和Q的方差固定为1，进行标准化，这有助于模型的识别。

代码解读5

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;

第五步，定义潜在交互项。例如，MV表示中介变量M与调节变量V的交互项。类似地，我们定义了所有需要的双向和三向交互项。

代码解读6

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);

第六步，定义结构模型中因变量Y受其他变量的影响。例如，Y受到M的影响，路径系数是b1，Y受到M和V的交互项MV的影响，路径系数是b2。以此类推，定义所有Y与其他变量之间的路径。

代码解读7

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);

第七步，定义结构模型中中介变量M受其他变量的影响。M受到X的影响，路径系数是a1，M受到W的影响，路径系数是a2，M受到X和W交互项XW的影响，路径系数是a3。

代码解读8

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);

第八步，开始定义模型约束，首先声明一些新的参数。这些参数将用来计算在不同调节变量水平下的条件效应。例如，LOW_W，MED_W，HIGH_W分别代表调节变量W的低、中、高水平，而ILWLVLQ代表W、V、Q都取低水平时的间接效应。

代码解读9

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;

第九步，设定调节变量的低、中、高水平的值。通常使用-1，0和1，代表一个标准差以下、平均值和标准差以上。

代码解读10

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;
ILWLVLQ = a1*b1 + a3*b1*LOW_W + a1*b2*LOW_V + a3*b2*LOW_W*LOW_V +
a1*b3*LOW_Q + a3*b3*LOW_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*LOW_W*LOW_V*LOW_Q;
IMWLVLQ = a1*b1 + a3*b1*MED_W + a1*b2*LOW_V + a3*b2*MED_W*LOW_V +
a1*b3*LOW_Q + a3*b3*MED_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*MED_W*LOW_V*LOW_Q;
...

第十步，计算不同调节变量水平组合下的条件间接效应。例如，ILWLVLQ表示当W、V和Q都取低值时的间接效应，计算公式是根据模型方程推导出来的。其他类似的参数也以此方式计算，我们这里只展示了两个，其他的计算方式类似。

代码解读11

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;
ILWLVLQ = a1*b1 + a3*b1*LOW_W + a1*b2*LOW_V + a3*b2*LOW_W*LOW_V +
a1*b3*LOW_Q + a3*b3*LOW_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*LOW_W*LOW_V*LOW_Q;
IMWLVLQ = a1*b1 + a3*b1*MED_W + a1*b2*LOW_V + a3*b2*MED_W*LOW_V +
a1*b3*LOW_Q + a3*b3*MED_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*MED_W*LOW_V*LOW_Q;
...
DLOV_LOQ = cdash1 + cdash4*LOW_V + cdash5*LOW_Q + cdash7*LOW_V*LOW_Q;
DMEV_LOQ = cdash1 + cdash4*MED_V + cdash5*LOW_Q + cdash7*MED_V*LOW_Q;
...

第十一步，计算不同调节变量水平组合下的条件直接效应。例如DLOV_LOQ表示当V和Q都取低值时的直接效应，其他类似。

代码解读12

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;
ILWLVLQ = a1*b1 + a3*b1*LOW_W + a1*b2*LOW_V + a3*b2*LOW_W*LOW_V +
a1*b3*LOW_Q + a3*b3*LOW_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*LOW_W*LOW_V*LOW_Q;
IMWLVLQ = a1*b1 + a3*b1*MED_W + a1*b2*LOW_V + a3*b2*MED_W*LOW_V +
a1*b3*LOW_Q + a3*b3*MED_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*MED_W*LOW_V*LOW_Q;
...
DLOV_LOQ = cdash1 + cdash4*LOW_V + cdash5*LOW_Q + cdash7*LOW_V*LOW_Q;
DMEV_LOQ = cdash1 + cdash4*MED_V + cdash5*LOW_Q + cdash7*MED_V*LOW_Q;
...
TLWLVLQ = ILWLVLQ + DLOV_LOQ;
TMWLVLQ = IMWLVLQ + DLOV_LOQ;
...

第十二步，计算不同调节变量水平组合下的条件总效应，等于对应的条件间接效应加上条件直接效应。例如TLWLVLQ等于ILWLVLQ加上DLOV_LOQ。

代码解读13

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;
ILWLVLQ = a1*b1 + a3*b1*LOW_W + a1*b2*LOW_V + a3*b2*LOW_W*LOW_V +
a1*b3*LOW_Q + a3*b3*LOW_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*LOW_W*LOW_V*LOW_Q;
IMWLVLQ = a1*b1 + a3*b1*MED_W + a1*b2*LOW_V + a3*b2*MED_W*LOW_V +
a1*b3*LOW_Q + a3*b3*MED_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*MED_W*LOW_V*LOW_Q;
...
DLOV_LOQ = cdash1 + cdash4*LOW_V + cdash5*LOW_Q + cdash7*LOW_V*LOW_Q;
DMEV_LOQ = cdash1 + cdash4*MED_V + cdash5*LOW_Q + cdash7*MED_V*LOW_Q;
...
TLWLVLQ = ILWLVLQ + DLOV_LOQ;
TMWLVLQ = IMWLVLQ + DLOV_LOQ;
...
PLOT(PLWLVLQ PMWLVLQ PHWLVLQ PLWMVLQ PMWMVLQ PHWMVLQ
PLWHVLQ PMWHVLQ PHWHVLQ
PLWLVMQ PMWLVMQ PHWLVMQ PLWMVMQ PMWMVMQ PHWMVMQ
PLWHVMQ PMWHVMQ PHWHVMQ
PLWLVHQ PMWLVHQ PHWLVHQ PLWMVHQ PMWMVHQ PHWMVHQ
PLWHVHQ PMWHVHQ PHWHVHQ);

第十三步，指定要绘制的图形。这些图形将展示条件间接效应随自变量变化的情况。

代码解读14

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;
ILWLVLQ = a1*b1 + a3*b1*LOW_W + a1*b2*LOW_V + a3*b2*LOW_W*LOW_V +
a1*b3*LOW_Q + a3*b3*LOW_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*LOW_W*LOW_V*LOW_Q;
IMWLVLQ = a1*b1 + a3*b1*MED_W + a1*b2*LOW_V + a3*b2*MED_W*LOW_V +
a1*b3*LOW_Q + a3*b3*MED_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*MED_W*LOW_V*LOW_Q;
...
DLOV_LOQ = cdash1 + cdash4*LOW_V + cdash5*LOW_Q + cdash7*LOW_V*LOW_Q;
DMEV_LOQ = cdash1 + cdash4*MED_V + cdash5*LOW_Q + cdash7*MED_V*LOW_Q;
...
TLWLVLQ = ILWLVLQ + DLOV_LOQ;
TMWLVLQ = IMWLVLQ + DLOV_LOQ;
...
PLOT(PLWLVLQ PMWLVLQ PHWLVLQ PLWMVLQ PMWMVLQ PHWMVLQ
PLWHVLQ PMWHVLQ PHWHVLQ
PLWLVMQ PMWLVMQ PHWLVMQ PLWMVMQ PMWMVMQ PHWMVMQ
PLWHVMQ PMWHVMQ PHWHVMQ
PLWLVHQ PMWLVHQ PHWLVHQ PLWMVHQ PMWMVHQ PHWMVHQ
PLWHVHQ PMWHVHQ PHWHVHQ);
LOOP(XVAL,-3,3,0.1);
PLWLVLQ = ILWLVLQ*XVAL;
PMWLVLQ = IMWLVLQ*XVAL;
...

第十四步，使用循环来改变自变量X的值，从-3到3，步长为0.1。然后根据计算出的条件间接效应，乘以自变量X的值，计算用于绘图的数据点。

代码解读15

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;
ILWLVLQ = a1*b1 + a3*b1*LOW_W + a1*b2*LOW_V + a3*b2*LOW_W*LOW_V +
a1*b3*LOW_Q + a3*b3*LOW_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*LOW_W*LOW_V*LOW_Q;
IMWLVLQ = a1*b1 + a3*b1*MED_W + a1*b2*LOW_V + a3*b2*MED_W*LOW_V +
a1*b3*LOW_Q + a3*b3*MED_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*MED_W*LOW_V*LOW_Q;
...
DLOV_LOQ = cdash1 + cdash4*LOW_V + cdash5*LOW_Q + cdash7*LOW_V*LOW_Q;
DMEV_LOQ = cdash1 + cdash4*MED_V + cdash5*LOW_Q + cdash7*MED_V*LOW_Q;
...
TLWLVLQ = ILWLVLQ + DLOV_LOQ;
TMWLVLQ = IMWLVLQ + DLOV_LOQ;
...
PLOT(PLWLVLQ PMWLVLQ PHWLVLQ PLWMVLQ PMWMVLQ PHWMVLQ
PLWHVLQ PMWHVLQ PHWHVLQ
PLWLVMQ PMWLVMQ PHWLVMQ PLWMVMQ PMWMVMQ PHWMVMQ
PLWHVMQ PMWHVMQ PHWHVMQ
PLWLVHQ PMWLVHQ PHWLVHQ PLWMVHQ PMWMVHQ PHWMVHQ
PLWHVHQ PMWHVHQ PHWHVHQ);
LOOP(XVAL,-3,3,0.1);
PLWLVLQ = ILWLVLQ*XVAL;
PMWLVLQ = IMWLVLQ*XVAL;
...
PLOT:
TYPE = plot2;

第十五步，指定绘图类型为plot2，它会绘制我们需要的曲线图。

代码解读16

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;
MODEL:
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
W BY W1* W2 W3 W4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1; V@1; Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XV | X XWITH V;
XQ | X XWITH Q;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XVQ | X XWITH VQ;
Y ON M (b1);
Y ON MV (b2);
Y ON MQ (b3);
Y ON MVQ (b4);
Y ON X (cdash1);
Y ON V (cdash2);
Y ON Q (cdash3);
Y ON XV (cdash4);
Y ON XQ (cdash5);
Y ON VQ (cdash6);
Y ON XVQ (cdash7);
M ON X (a1);
M ON W (a2);
M ON XW (a3);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q
ILWLVLQ IMWLVLQ IHWLVLQ ILWMVLQ IMWMVLQ IHWMVLQ
ILWHVLQ IMWHVLQ IHWHVLQ
ILWLVMQ IMWLVMQ IHWLVMQ ILWMVMQ IMWMVMQ IHWMVMQ
ILWHVMQ IMWHVMQ IHWHVMQ
ILWLVHQ IMWLVHQ IHWLVHQ ILWMVHQ IMWMVHQ IHWMVHQ
ILWHVHQ IMWHVHQ IHWHVHQ
DLOV_LOQ DMEV_LOQ DHIV_LOQ DLOV_MEQ DMEV_MEQ DHIV_MEQ
DLOV_HIQ DMEV_HIQ DHIV_HIQ
TLWLVLQ TMWLVLQ THWLVLQ TLWMVLQ TMWMVLQ THWMVLQ
TLWHVLQ TMWHVLQ THWHVLQ
TLWLVMQ TMWLVMQ THWLVMQ TLWMVMQ TMWMVMQ THWMVMQ
TLWHVMQ TMWHVMQ THWHVMQ
TLWLVHQ TMWLVHQ THWLVHQ TLWMVHQ TMWMVHQ THWMVHQ
TLWHVHQ TMWHVHQ THWHVHQ);
LOW_W = -1;
MED_W = 0;
HIGH_W = 1;
LOW_V = -1;
MED_V = 0;
HIGH_V = 1;
LOW_Q = -1;
MED_Q = 0;
HIGH_Q = 1;
ILWLVLQ = a1*b1 + a3*b1*LOW_W + a1*b2*LOW_V + a3*b2*LOW_W*LOW_V +
a1*b3*LOW_Q + a3*b3*LOW_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*LOW_W*LOW_V*LOW_Q;
IMWLVLQ = a1*b1 + a3*b1*MED_W + a1*b2*LOW_V + a3*b2*MED_W*LOW_V +
a1*b3*LOW_Q + a3*b3*MED_W*LOW_Q + a1*b4*LOW_V*LOW_Q +
a3*b4*MED_W*LOW_V*LOW_Q;
...
DLOV_LOQ = cdash1 + cdash4*LOW_V + cdash5*LOW_Q + cdash7*LOW_V*LOW_Q;
DMEV_LOQ = cdash1 + cdash4*MED_V + cdash5*LOW_Q + cdash7*MED_V*LOW_Q;
...
TLWLVLQ = ILWLVLQ + DLOV_LOQ;
TMWLVLQ = IMWLVLQ + DLOV_LOQ;
...
PLOT(PLWLVLQ PMWLVLQ PHWLVLQ PLWMVLQ PMWMVLQ PHWMVLQ
PLWHVLQ PMWHVLQ PHWHVLQ
PLWLVMQ PMWLVMQ PHWLVMQ PLWMVMQ PMWMVMQ PHWMVMQ
PLWHVMQ PMWHVMQ PHWHVMQ
PLWLVHQ PMWLVHQ PHWLVHQ PLWMVHQ PMWMVHQ PHWMVHQ
PLWHVHQ PMWHVHQ PHWHVHQ);
LOOP(XVAL,-3,3,0.1);
PLWLVLQ = ILWLVLQ*XVAL;
PMWLVLQ = IMWLVLQ*XVAL;
...
PLOT:
TYPE = plot2;
OUTPUT:
CINT;

最后一步，指定输出置信区间，这有助于我们评估模型参数的可靠性。

资源汇总

• 本视频讲义地址: https://mlln.cn/mplus-model-templates/model38latent.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

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