我正在用一个潜在变量（认知）在lavaan中构建一个潜在结构方程模型，对它进行两次测量。现在，我想添加一个 Dimension 组变量，该变量基本上是李克特尺度的变量（1 =负数到7 =正数）。该变量应该只对潜伏变化分数（dextra1）产生影响。如果变量像性别这样分类，我将建立一个多组模型，但是在这种情况下，我不知道该怎么做。 我应该在哪里添加尺寸变量来影响潜在变化得分（dextra1）？
以下是OSF的示例，其中先模拟然后拟合数据。
##### Simulate data for a multiple indicator Univariate Latent Change Score model ####
Fix sample size samplesize<-500 Simulate data for a Univariate Latent Change Score model.
MILCS_simulate<-'
#### The following two lines specify the measurement model for multiple indicators (X1-X3)
#### measured on two occasions (T1-T2)
COG_T1=~.8*T1X1+.9*T1X2+.7*T1X3 # This specifies the measurement model for COG_T1
COG_T2=~.8*T2X1+.9*T2X2+.7*T2X3 # This specifies the measurement model for COG_T2
##### The following lines specify the core assumptions of the LCS
##### and should not generally be modified
COG_T2 ~ 1*COG_T1 # Fixed regression of COG_T2 on COG_T1
dCOG1 =~ 1*COG_T2 # Fixed regression of dCOG1 on COG_T2
COG_T2 ~ 0*1 # This line constrains the intercept of COG_T2 to 0
COG_T2 ~~ 0*COG_T2 # This fixes the variance of the COG_T2 to 0
T1X1~0*1 # This fixes the intercept of X1 to 0
T1X2~1*1 # This fixes the intercept of X2 to 1
T1X3~.5*1 # This fixes the intercept of X3 to 0.5
T2X1~0*1 # This fixes the intercept of X1 to 0
T2X2~1*1 # This fixes the intercept of X2 to 1
T2X3~.5*1 # This fixes the intercept of X3 to 0.5
###### The following five parameters will be estimated in the model.
###### Values can be modified manually to examine the effect on the model
dCOG1 ~ 10*1 # This fixes the intercept of the change score to 10
COG_T1 ~ 50*1 # This fixes the intercept of COG_T1 to 50.
dCOG1 ~~ 5*dCOG1 # This fixes the variance of the change scores to 5.
COG_T1 ~~ 8*COG_T1 # This fixes the variance of the COG_T1 to 8.
dCOG1~-0.1*COG_T1 # This fixes the self-feedback parameter to -0.1.
'
#Simulate data
set.seed(1234)
simdatMILCS<-simulateData(MILCS_simulate,sample.nobs = samplesize,meanstructure = T) #Simulate data
colMeans(simdatMILCS) #sanity check the means
write.csv(simdatMILCS,'2_simdatMILCS.csv')
#Fit the multiple indicator Univariate Latent Change Score model to simulated data MILCS<-'
COG_T1=~1*T1X1+T1X2+T1X3 # This specifies the measurement model for COG_T1
COG_T2=~1*T2X1+equal("COG_T1=~T1X2")*T2X2+equal("COG_T1=~T1X3")*T2X3 # This specifies the measurement model for COG_T2 with the equality constrained factor loadings
COG_T2 ~ 1*COG_T1 # Fixed regression of COG_T2 on COG_T1
dCOG1 =~ 1*COG_T2 # Fixed regression of dCOG1 on COG_T2
COG_T2 ~ 0*1 # This line constrains the intercept of COG_T2 to 0
COG_T2 ~~ 0*COG_T2 # This fixes the variance of the COG_T2 to 0
dCOG1 ~ 1 # This estimates the intercept of the change score
COG_T1 ~ 1 # This estimates the intercept of COG_T1
dCOG1 ~~ dCOG1 # This estimates the variance of the change scores
COG_T1 ~~ COG_T1 # This estimates the variance of the COG_T1
dCOG1~COG_T1 # This estimates the self-feedback parameter
T1X1~~T2X1 # This allows residual covariance on indicator X1 across T1 and T2
T1X2~~T2X2 # This allows residual covariance on indicator X2 across T1 and T2
T1X3~~T2X3 # This allows residual covariance on indicator X3 across T1 and T2
T1X1~~T1X1 # This allows residual variance on indicator X1
T1X2~~T1X2 # This allows residual variance on indicator X2
T1X3~~T1X3 # This allows residual variance on indicator X3
T2X1~~equal("T1X1~~T1X1")*T2X1 # This allows residual variance on indicator X1 at T2
T2X2~~equal("T1X2~~T1X2")*T2X2 # This allows residual variance on indicator X2 at T2
T2X3~~equal("T1X3~~T1X3")*T2X3 # This allows residual variance on indicator X3 at T2
T1X1~0*1 # This constrains the intercept of X1 to 0 at T1
T1X2~1 # This estimates the intercept of X2 at T1
T1X3~1 # This estimates the intercept of X3 at T1
T2X1~0*1 # This constrains the intercept of X1 to 0 at T2
T2X2~equal("T1X2~1")*1 # This estimates the intercept of X2 at T2
T2X3~equal("T1X3~1")*1 # This estimates the intercept of X3 at T2
'