I would like to clarify which chi-square values to use for multiple group using MLR.
For the unconstrined model, do I use chi-square test of model fit? Or the chi-square test of model fit for the baseline model?
I have used the chi-square test of model fit from the unconstrained model and the the chi-square test of model fit from the constrained model.
There is a scaling corection factor for MLR under Loglikelihood for H0 - is this the one that is used?
For the unconstrained model (where paths are set to vary, thus not equal), I keep getting chi-square = 0, degree of freedom = o, p = .000. I am wondering why this is and does it make sense to compute the chi-square test with the scaling correction for MLR as per formula on the website or do I skip that given the 0 value for chi-square. Scaling correction is 1.000.
Jean FRISOU posted on Wednesday, October 02, 2013 - 8:04 am
I am testing the equality of the average slopes of growth model, with two groups (women and men) and estimator MLMV. The procedure is DIFFTEST. In the output file the following message is indicated: "THE MODEL ESTIMATION TERMINATED NORMALLY THE CHI-SQUARE COMPUTATION COULD NOT BE COMPLETED BECAUSE OF A SINGULAR MATRIX." Only the test of differences is not displayed. Are there alternative ways to DIFFTEST for this test?
Please send the output from the second-order model to Support with your license number.
Ryan Snead posted on Friday, September 15, 2017 - 8:27 am
I wanted to ask about reporting chi-square testing for a path model using TYPE=COMPLEX. Below is my output.
Value = 9.139* DF = 9 P-Value = 0.4245 Scaling Correction Factor for MLR = 1.0656
*The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used for chi-square difference testing in the regular way. MLM, MLR and WLSM chi-square difference testing is described on the Mplus website. MLMV, WLSMV, and ULSMV difference testing is done using the DIFFTEST option.
We receive this warning for our test which confuses me. The description found in the user guide regarding our estimator states:
“MLR – maximum likelihood parameter estimates with standard errors and a chi-square test statistic (when applicable) that are robust to non-normality and non-independence of observations when used with TYPE=COMPLEX. The MLR standard errors are computed using a sandwich estimator. The MLR chi-square test statistic is asymptotically equivalent to the Yuan-Bentler T2* test statistic.”
Based on this quote, I assume that the value in the output is supposed to be accurate, however, the warning seems to state otherwise. The website references a formula for this scenario. Should I instead perform manual calculations? If not and the statistic is adequate as is, what does it mean to have a non-significant chi-square test while all of our other fit indices indicate a “good fitting” model?