Mplus model57latent 模型讲解

来自图书《MPlus中介调节模型》

使用Mplus构建中介、调节和调节中介模型

• 理论模型
• 数学模型
• 数学推导
• 代码解读

大家好，欢迎来到本期教程。本教程将手把手教大家如何使用Mplus构建复杂统计模型，包括中介模型、调节模型和调节中介模型，以及它们的潜变量版本。

我们不会面面俱到地讲解Mplus的所有功能，而是聚焦于实际应用，提供一系列可以直接使用的代码案例。 每个案例都包含四个部分：首先，我们会讲解模型的理论基础，也就是理论模型；然后，我们会用数学公式表达模型，这就是数学模型；对于一些复杂的模型，我们会进行数学推导，帮助大家理解模型的逻辑；最后，我们会详细解读Mplus的代码，让大家能够轻松上手操作。

通过学习本教程，您可以快速掌握Mplus在处理中介、调节和调节中介模型方面的强大功能，并能够将这些方法应用到自己的研究中。 希望本教程能成为您学习和使用Mplus的便捷工具！

理论模型

我们来看这张图，它展示了一个比较复杂的统计模型。这个模型包含一个自变量X，一个因变量Y，以及一个中介变量M。此外，还有四个调节变量W、Z、V和Q，它们会影响自变量和因变量之间的关系。箭头表示变量之间的影响方向，比如，X会影响M，M会影响Y，而W、Z、V、Q则会分别调节X对M的影响，以及M对Y的影响。 我们接下来会用数学模型来更精确地描述这种关系，并讲解如何用Mplus来分析这种模型。

数学模型

这张图展示的是一个复杂的调节中介模型。我们看到，X是自变量，Y是因变量，M是中介变量，W、Z、V、Q是四个调节变量。 X通过M间接影响Y，同时也存在X对Y的直接影响。 更重要的是，这两种影响都会受到W、Z、V、Q四个调节变量的影响，甚至可能存在多达三阶的交互作用。图中箭头表示变量间的影响方向，数字代表具体的路径系数，这些系数会在后续分析中被估计。 这个模型反映了变量间错综复杂的关系，我们将通过Mplus来分析这些关系，并解读其中的因果关系。

数学公式1

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ

这是我们的初始模型方程，其中Y是因变量，X是自变量，M, V, Q, W, Z是其他变量。

数学公式2

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
M = a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ

这是中间变量M的表达式，它与自变量X以及其他变量W, Z相关。

数学公式3

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
M = a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ
Y = b0 + b1(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ) + b2(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)V + b3(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)Q + b4(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)VQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ

将M的表达式代入Y的表达式中。

数学公式4

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
M = a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ
Y = b0 + b1(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ) + b2(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)V + b3(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)Q + b4(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)VQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
Y = b0 + a0b1 + a1b1X + a2b1W + a3b1Z + a4b1XW + a5b1XZ + a6b1WZ + a7b1XWZ + a0b2V + a1b2XV + a2b2WV + a3b2ZV + a4b2XWV + a5b2XZV + a6b2WZV + a7b2XWZV + a0b3Q + a1b3XQ + a2b3WQ + a3b3ZQ + a4b3XWQ + a5b3XZQ + a6b3WZQ + a7b3XWZQ + a0b4VQ + a1b4XVQ + a2b4WVQ + a3b4ZVQ + a4b4XWVQ + a5b4XZVQ + a6b4WZVQ + a7b4XWZVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ

展开括号后，得到一个非常复杂的表达式。

数学公式5

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
M = a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ
Y = b0 + b1(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ) + b2(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)V + b3(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)Q + b4(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)VQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
Y = b0 + a0b1 + a1b1X + a2b1W + a3b1Z + a4b1XW + a5b1XZ + a6b1WZ + a7b1XWZ + a0b2V + a1b2XV + a2b2WV + a3b2ZV + a4b2XWV + a5b2XZV + a6b2WZV + a7b2XWZV + a0b3Q + a1b3XQ + a2b3WQ + a3b3ZQ + a4b3XWQ + a5b3XZQ + a6b3WZQ + a7b3XWZQ + a0b4VQ + a1b4XVQ + a2b4WVQ + a3b4ZVQ + a4b4XWVQ + a5b4XZVQ + a6b4WZVQ + a7b4XWZVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
Y = (b0 + a0b1 + a2b1W + a3b1Z + a6b1WZ + a0b2V + a2b2WV + a3b2ZV + a6b2WZV + a0b3Q + a2b3WQ + a3b3ZQ + a6b3WZQ + a0b4VQ + a2b4WVQ + a3b4ZVQ + a6b4WZVQ + c2'W + c3'Z + c6'WZ + c8'V + c9'Q + c12'VQ) + (a1b1 + a4b1W + a5b1Z + a7b1WZ + a1b2V + a4b2WV + a5b2ZV + a7b2WZV + a1b3Q + a4b3WQ + a5b3ZQ + a7b3WZQ + a1b4VQ + a4b4WVQ + a5b4ZVQ + a7b4WZVQ + c1' + c4'W + c5'Z + c7'WZ + c10'V + c11'Q + c13'VQ)X

整理成Y = a + bX的形式，其中a和b是W, Z, V, Q的函数。

数学公式6

Y = b0 + b1M + b2MV + b3MQ + b4MVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
M = a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ
Y = b0 + b1(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ) + b2(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)V + b3(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)Q + b4(a0 + a1X + a2W + a3Z + a4XW + a5XZ + a6WZ + a7XWZ)VQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
Y = b0 + a0b1 + a1b1X + a2b1W + a3b1Z + a4b1XW + a5b1XZ + a6b1WZ + a7b1XWZ + a0b2V + a1b2XV + a2b2WV + a3b2ZV + a4b2XWV + a5b2XZV + a6b2WZV + a7b2XWZV + a0b3Q + a1b3XQ + a2b3WQ + a3b3ZQ + a4b3XWQ + a5b3XZQ + a6b3WZQ + a7b3XWZQ + a0b4VQ + a1b4XVQ + a2b4WVQ + a3b4ZVQ + a4b4XWVQ + a5b4XZVQ + a6b4WZVQ + a7b4XWZVQ + c1'X + c2'W + c3'Z + c4'XW + c5'XZ + c6'WZ + c7'XWZ + c8'V + c9'Q + c10'XV + c11'XQ + c12'VQ + c13'XVQ
Y = (b0 + a0b1 + a2b1W + a3b1Z + a6b1WZ + a0b2V + a2b2WV + a3b2ZV + a6b2WZV + a0b3Q + a2b3WQ + a3b3ZQ + a6b3WZQ + a0b4VQ + a2b4WVQ + a3b4ZVQ + a6b4WZVQ + c2'W + c3'Z + c6'WZ + c8'V + c9'Q + c12'VQ) + (a1b1 + a4b1W + a5b1Z + a7b1WZ + a1b2V + a4b2WV + a5b2ZV + a7b2WZV + a1b3Q + a4b3WQ + a5b3ZQ + a7b3WZQ + a1b4VQ + a4b4WVQ + a5b4ZVQ + a7b4WZVQ + c1' + c4'W + c5'Z + c7'WZ + c10'V + c11'Q + c13'VQ)X
间接效应：(a1 + a4W + a5Z + a7WZ)(b1 + b2V + b3Q + b4VQ)
直接效应：c1' + c4'W + c5'Z + c7'WZ + c10'V + c11'Q + c13'VQ

最终，我们将效应分解为间接效应和直接效应，条件于W, Z, V, Q。

代码解读1

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;

首先，我们定义了要使用的变量。这里包含了四个测量潜变量X、M、Y以及四个中介变量W、Z、V、Q的指标变量。 大家可以看到，每个潜变量都对应四个指标变量。

代码解读2

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 V1 V2 V3 V4 Q1 Q2 Q3 Q4
Y1 Y2 Y3 Y4;
ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;

接下来是分析设置。我们选择一般随机效应模型，使用最大似然估计法，并指定使用积分算法。积分算法对于处理模型中的非线性部分非常重要。

代码解读3

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;

这一部分定义了测量模型。 我们指定了每个潜变量与其指标变量之间的关系。 注意，W1后面有个星号，表示其载荷被固定为1，用来标准化潜变量。

代码解读4

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1;  Z@1;  V@1;  Q@1;

为了标准化中介变量，我们将W、Z、V、Q的方差固定为1。

代码解读5

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1;  Z@1;  V@1;  Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XZ | X XWITH Z;
XV | X XWITH V;
XQ | X XWITH Q;
WZ | W XWITH Z;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XWZ | X XWITH WZ;
XVQ | X XWITH VQ;

这里定义了潜变量之间的交互作用项，例如MV代表M和V的交互作用。 这使得我们可以考察中介变量之间的交互效应。

代码解读6

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1;  Z@1;  V@1;  Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XZ | X XWITH Z;
XV | X XWITH V;
XQ | X XWITH Q;
WZ | W XWITH Z;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XWZ | X XWITH WZ;
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 W (cdash2);
Y ON Z (cdash3);
Y ON XW (cdash4);
Y ON XZ (cdash5);
Y ON WZ (cdash6);
Y ON XWZ (cdash7);
Y ON V (cdash8);
Y ON Q (cdash9);
Y ON XV (cdash10);
Y ON XQ (cdash11);
Y ON VQ (cdash12);
Y ON XVQ (cdash13);

这一部分定义了结构模型，展示了Y如何被X、M以及它们之间的交互作用所预测。 我们用b1, b2等命名回归系数，便于后续分析。

代码解读7

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1;  Z@1;  V@1;  Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XZ | X XWITH Z;
XV | X XWITH V;
XQ | X XWITH Q;
WZ | W XWITH Z;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XWZ | X XWITH WZ;
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 W (cdash2);
Y ON Z (cdash3);
Y ON XW (cdash4);
Y ON XZ (cdash5);
Y ON WZ (cdash6);
Y ON XWZ (cdash7);
Y ON V (cdash8);
Y ON Q (cdash9);
Y ON XV (cdash10);
Y ON XQ (cdash11);
Y ON VQ (cdash12);
Y ON XVQ (cdash13);
M ON X (a1);
M ON W (a2);
M ON Z (a3);
M ON XW (a4);
M ON XZ (a5);
M ON WZ (a6);
M ON XWZ (a7);

同样，这里定义了中介变量M如何被X以及其他变量预测。

代码解读8

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1;  Z@1;  V@1;  Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XZ | X XWITH Z;
XV | X XWITH V;
XQ | X XWITH Q;
WZ | W XWITH Z;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XWZ | X XWITH WZ;
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 W (cdash2);
Y ON Z (cdash3);
Y ON XW (cdash4);
Y ON XZ (cdash5);
Y ON WZ (cdash6);
Y ON XWZ (cdash7);
Y ON V (cdash8);
Y ON Q (cdash9);
Y ON XV (cdash10);
Y ON XQ (cdash11);
Y ON VQ (cdash12);
Y ON XVQ (cdash13);
M ON X (a1);
M ON W (a2);
M ON Z (a3);
M ON XW (a4);
M ON XZ (a5);
M ON WZ (a6);
M ON XWZ (a7);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_Z MED_Z HIGH_Z LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q ...); 
LOW_W = -1; ... HIGH_Q = 1;
ILLLL = a1*b1 + ...;

模型约束部分是这部分代码的核心。我们定义了新的参数，代表在不同调节变量值组合下的效应，并通过公式计算这些效应。 这里省略了大量的公式，这些公式计算了所有可能的调节组合下的间接效应，直接效应和总效应。

代码解读9

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1;  Z@1;  V@1;  Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XZ | X XWITH Z;
XV | X XWITH V;
XQ | X XWITH Q;
WZ | W XWITH Z;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XWZ | X XWITH WZ;
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 W (cdash2);
Y ON Z (cdash3);
Y ON XW (cdash4);
Y ON XZ (cdash5);
Y ON WZ (cdash6);
Y ON XWZ (cdash7);
Y ON V (cdash8);
Y ON Q (cdash9);
Y ON XV (cdash10);
Y ON XQ (cdash11);
Y ON VQ (cdash12);
Y ON XVQ (cdash13);
M ON X (a1);
M ON W (a2);
M ON Z (a3);
M ON XW (a4);
M ON XZ (a5);
M ON WZ (a6);
M ON XWZ (a7);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_Z MED_Z HIGH_Z LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q ...); 
LOW_W = -1; ... HIGH_Q = 1;
ILLLL = a1*b1 + ...;
PLOT(PLLLL PMLLL PHLLL ...);
LOOP(XVAL,-3,3,0.1);
PLLLL = ILLLL*XVAL; ...

最后，我们设置绘图参数，循环计算不同X值下的条件间接效应，并最终生成图形。

代码解读10

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
W1 W2 W3 W4 Z1 Z2 Z3 Z4 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;
Z BY Z1* Z2 Z3 Z4;
V BY V1* V2 V3 V4;
Q BY Q1* Q2 Q3 Q4;
Y BY Y1 Y2 Y3 Y4;
W@1;  Z@1;  V@1;  Q@1;
MV | M XWITH V;
MQ | M XWITH Q;
XW | X XWITH W;
XZ | X XWITH Z;
XV | X XWITH V;
XQ | X XWITH Q;
WZ | W XWITH Z;
VQ | V XWITH Q;
MVQ | M XWITH VQ;
XWZ | X XWITH WZ;
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 W (cdash2);
Y ON Z (cdash3);
Y ON XW (cdash4);
Y ON XZ (cdash5);
Y ON WZ (cdash6);
Y ON XWZ (cdash7);
Y ON V (cdash8);
Y ON Q (cdash9);
Y ON XV (cdash10);
Y ON XQ (cdash11);
Y ON VQ (cdash12);
Y ON XVQ (cdash13);
M ON X (a1);
M ON W (a2);
M ON Z (a3);
M ON XW (a4);
M ON XZ (a5);
M ON WZ (a6);
M ON XWZ (a7);
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W LOW_Z MED_Z HIGH_Z LOW_V MED_V HIGH_V LOW_Q MED_Q HIGH_Q ...); 
LOW_W = -1; ... HIGH_Q = 1;
ILLLL = a1*b1 + ...;
PLOT(PLLLL PMLLL PHLLL ...);
LOOP(XVAL,-3,3,0.1);
PLLLL = ILLLL*XVAL; ...
PLOT: TYPE = plot2;
OUTPUT: CINT;

指定绘图类型和输出内容，包含置信区间。

资源汇总

• 本视频讲义地址: https://mlln.cn/mplus-model-templates/model57latent.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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