I try to show scalar invariance across times (Pre-/Posttest) in one group. I have two factors and 4 item-parcels per factor.
The configural and metric models are ok. Unfortunately there is a problem with the scalar invariance. The model fit seems ok, but the intercepts look really strange.
I fixed the first factor loading to one (the others are free). I fixed also the first intercepts of the fixed loading items to zero. The problem is that the intercepts in the scalar invariance output are really different to the intercepts in the metric invariance output.
Hi, Dr. Muthen! When assessing indices during scalar invariance testing (for which intercepts are constrained), is it useful to attend to indices related to latent means? I guess the same question goes for other invariance tests - should one only attend to indices for imposed constraints, or is there reason that some constraints might affect other estimates?
When intercepts are free, factor means must be fixed to zero.
Jone Aliri posted on Tuesday, July 17, 2018 - 10:07 am
I have an ESEM with target rotation (categorical items) and I want to test sex invariance. If I use the "configural metric scalar" syntax I have an error because it cannot calculate the metric model, but if I put only "configural scalar" I can calculate the fit of the configural and the scalar models and calculate the diftest.
I am working with a group assessing measurement invariance of a single-factor 13-item scale with items measured on 5-points. We are treating these items as ordinal and using the WLSMV estimator. Thanks to Mplus's invariance assessment features, the team can readily obtain chi-square statistics comparing configural to metric, configural to scalar, and metric to scalar models. The configural-metric comparison is NS (chi-square=17.52, p=.49), yet the metric-scalar comparison is significant at chi-square(38)=106.64, p<.0001. However, the various descriptive fit statistics are all highly similar across the three models, i.e., RMSEA=.06,.05.04; CFI=.993,.993,.991; SRMR=.024,.024,.026.
I am trying to figure out how to advise the team in weighing the chi-square test evidence, which suggests metric invariance is upheld but not scalar invariance, versus the descriptive fit information, which seems to suggest the three models are approximately equivalent. We have found one article by Cheung & Rensvold, 2002, in the SEM Journal, which address goodness of fit indices for invariance comparisons, but your opinions + suggestions for any additional literature touching on this topic would be much appreciated. Ditto for any alternative approaches available in Mplus we could also consider (e.g., alignment to assess approximate invariance if appropriate for this context) would also be appreciated.