Questions about the use of estimation

[Chen Ali]

 

 

I have questions about the use of estimation. I checked the univariate and multivariate normality for 6 items that are 5-point Likert scale.

However, univariate and multivariate normality were violated. Therefore, I used MLR and WLSMV estimators to run 1-factor CFA when I treated the data are continuous. It turned out that WLSMV showed the best model fit compared with MLR : CFI:0.994,TLI:0.988, RMSEA:0.052,SRMS=0.041, and loadings are high.

 

In addition, I used WLSMV estimators to run 1-factor CFA when I treated the data are categorical. The results showed that CFI: 0.994, TLI:0.990, RMSEA:0.108, SRMS=0.044 and loadings are high. The value of RMSEA is too high compared with the situation where I used WLSMV estimation to treat the data are continuous.

 

My question is that : Could WLSMV be applied in the continuous data (i.e. 5-pint Likert scale) when data are not normal? Because I thought that WLSMV only could be applied in the categorical data.

[Terrence Jorgensen]

Could WLSMV be applied in the continuous data (i.e. 5-pint Likert scale) when data are not normal? Because I thought that WLSMV only could be applied in the categorical data.

WLSMV is just a keyword used as a shortcut for the lavaan arguments

estimator = "DWLS" # only used for categorical data
se = "robust"
test = "scaled.shifted"

WLS can be used for continuous data, but the robust procedures are only available for DWLS with categorical data.

Model fit is not comparable between the different estimators because that are finding solutions to reproduce different data (means+covariances of continuous data vs. thresholds+polychoric correlations for categorical data).  So that is not the basis for choosing an estimator.  

Terrence D. Jorgensen

Assistant Professor, Methods and Statistics

Research Institute for Child Development and Education, the University of Amsterdam

http://www.uva.nl/profile/t.d.jorgensen

[Chen Ali]

Thank you!

I am still a bit confused about WLSMV. Could WLSMV be applied in continuous data as well as categorical data?

 

In the following code, I used the WLSMV estimator, but it did not treat data as categorical data:

model<-'QoLIBRI=~q10+q11+q12+q13+q14+q15'
fit_WLSMV<-cfa(model=model,data=All,estimator="WLSMV")

 

Another code,I used the WLSMV estimator, but it treated data as categorical data:

model<-'QoLIBRI=~q10+q11+q12+q13+q14+q15'
fit_WLSMV<-cfa(model=model,data=All,ordered =Items,estimator="WLSMV")

 

Also, are the following two codes using the same WLSMV estimation based on the same CFA model?

 

model<-'QoLIBRI=~q10+q11+q12+q13+q14+q15'

fit_WLSMV<-cfa(model=model,data=All,ordered =Items,estimator="WLSMV")

fit_mplus<-cfa(model = model,estimator="DWLS", se = "robust.sem", test = "scaled.shifted",parameterization="theta",

               ordered = Items,data=All)

Terrence Jorgensen於 2019年6月7日星期五 UTC+2下午4時28分45秒寫道：

[Terrence Jorgensen]

Could WLSMV be applied in continuous data as well as categorical data?

Not without tricking the program into it.  Why would you want to?  DWLS and theta parameterization were developed for the application of covariance structure models to ordinal data.