You can use either. If you have several factors, WLSMV is best because with ML each factor with binary factor indicators requires one dimension of integration. If you want to include residual covariances between factor indicators, WLSMV is also best because with ML each residual covariance requires one dimension of integration. Models using more than four dimensions of integration are not recommended.
Scott Smith posted on Monday, October 14, 2013 - 11:25 am
Can you further explain what dimensions of integration are? Does this mean that I shouldn't run more than four factors within one CFA model at a time?
I'm performing CFA with all binary variables. I have a problem becouse I have to set to 1 the variances of the three latent variables (all latent variables) to estimate the model, and I think that this is the reason to obtain the same results in Model results and Standardized estimates.
I need the results of the STDYX output for this model and I have 7 standarized estimates greater than 1, e.g x4 has a estimate=1.141 and S.E.=0.177
I´m wondering if you know some formula to fix the standardized estimates in the STDYX and obtain values less than 1.
Standardized coefficients can be greater than one. See the FAQ on our website.
Danica Cruz posted on Sunday, August 03, 2014 - 6:53 pm
I read in the Mplus User's Guide that it's possible to have combinations of continuous, binary and categorical indicators in a measurement model. Is it possible to have these combinations on the same factor? For example a binary variable and two categorical variables (one with 4 levels and one with 5 levels)? If so, how would the scaling work? Thank you.
I'm working with an instrument with 10 dimensions (151 binary items). Some of the items belong to more than one factor, what can I do? I tried doing a common CFA for binary items, after 7 hours of working the output said "NO CONVERGENCE. NUMBER OF ITERATIONS EXCEEDED". What can I do?
Hi. I am trying to do a monte carlo simulation to determine an appropriate sample size for the estimation of a CFA model with a single latent variable measured by 8 binary variables. However, Im having trouble finding a way to do the CFA simulation with all binary variables.
Using the example mcex5.2 as a template (montecarlo example for a 2 factor CFA, all binary variables) I tried the following:
montecarlo: names = u1-u8; generate = u1-u8 (1); categorical = u1-u8; nobs = 300; nreps = 10000; model montecarlo: f1 by u1@1 u2-u8*.8; u1-u8*.2; f1*.8; model: f1 by u1@1 u2-u8*.8; u1-u8*.2; f1*.8; output: tech9;
The result is the following:
Variances for categorical outcomes can only be specified using PARAMETERIZATION=THETA with estimators WLS, WLSM, or WLSMV.
After adding ANALYSIS: PARAMETERIZATION=THETA, I get these error messages for each replication:
THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL.
Is there a problem with my use of the mplus language? Or is there something inviable about what I want to do?
Also, I understand that the simulation can be carried out with ML/MLR, but I don’t know about the pertinence of using numerical integration.
Note from mcex5.2 that the Model command drops the line with residual variances that is used in Model Population.
Residual variances with binary outcomes are only identified with multiple groups or time points.
Note also that your parameters values in Model Population don't make the latent response variable variances add to 1 as is assumed for the default Delta parameterization of WLSMV. With factor loading 0.8, factor variance 0.8, and residual variance 0.2, the latent response variable variance is 0.64*0.8 + 0.2 which is not 1. This makes the estimates to come out in the wrong metric.
ML/MLR would be fine here - then you don't mention the residual variances in Model Population either.