如何解决如何在R中的lavaan中调整Cronbach的Alpha测量误差
谁能建议使用R中的lavaan包如何最好地解决测量误差(由Cronbach的alpha度量)?下面是一个完整的示例。该示例尝试根据他们的外向程度预测某人的朋友数量。请注意,我在代码末尾尝试解决外向度的测量错误的尝试失败。
#Setup dataset with 10000 observations (individuals).
set.seed(1226)
np <- 10000
df <- data.frame(matrix(0,np,0))
#Create 'true' extraversion with mean=5,SD=0.5.
df$TruE <- round(rnorm(np,5,0.5),0)
#Create Friends variable,making it depend on Extraversion such that
#for every 1 unit increase in extraversion,the person has +3 friends.
df$Frnd <- 3*df$TruE+round(rnorm(np,5),0)
#Create items that attempt to measure true extraversion (TruE).
df$ext1 <- df$TruE+round(rnorm(np,0.8),0)
df$ext2 <- df$TruE+round(rnorm(np,0)
df$ext3 <- df$TruE+round(rnorm(np,0)
df$ext4 <- df$TruE+round(rnorm(np,0)
df$ext5 <- df$TruE+round(rnorm(np,0)
multi.hist(df[,c("ext1","ext2","ext3","ext4","ext5")])
#Create latent variable that attempts to measure extraversion from the items.
df$LatE <- rowMeans(df[,"ext5")])
#Check correlation matrix.
round(cor(df),2)
#Check Cronbach alpha for latent variable.
psych::alpha(df[,"ext5")])
#Recover the true model using standard regression (works as expected).
summary(lm(Frnd~TruE,data=df))
#Attempt to recover the true model based on the latent variable using standard regression.
summary(lm(Frnd~LatE,data=df)) #Not correct due to poor reliability
#Using lavaan with latent variable modelling (works very well).
Mod1 <- 'LatE2 =~ ext1 + ext2 + ext3 + ext4 + ext5
Frnd ~ LatE2
'
Fit1 <- lavaan::sem(Mod1,data=df)
summary(Fit1,standardized=TRUE)
#Attempt to adjust for Cronbach alpha (doesn't work).
Mod2 <- 'Frnd ~ (1-.69)*LatE
'
Fit2 <- lavaan::sem(Mod2,data=df)
summary(Fit2,standardized=TRUE)
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