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I read the chapter decribing these features but I found it too sophisticated for me. MLM and MLR chi-square difference testing is described on page 360 in the Mplus User's Guide." Everything looks fine except I am not sure I understand clearly the warning "* The chi-square value for MLM, MLR, MLMV, WLSM and WLSMV cannot be used for chi-square difference tests. I am doing a series of CFAs with ordinal variables using Mplus 2.12. Mesfin Mulatu posted on Monday, Septem8:24 am There are 26 thresholds and 13 thresholds parameters estimated which I think explains the difference of 13. In such a model, thresholds do influence the degrees of freedom. You imply that you have thresholds in the model and that you hold them equal across time. The difference in the degrees of freedom you expect and the degrees of freedom that you get is 293 versus 306 or a difference of 13. Furthermore, I'm assuming that my imposing equal thresholds for items involved in the longitudinal model is in no way associated with computing df.ĭoes anyone see an error in my rationale? Further, has anyone else doing longitudinal CFA on dichtomous items noted a problem in computing K. I'm assuming that WLSM doesn't adjust df (unlike WLSMV).
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Had 306 df been correct, shouldn't the resulting model be eval at 317 df (306+11)? However my output file evaluates the chi sq 306 df.įinally when I specify another model that imposes equality constraints for each factor loading at 2 points in time (that is 11 factor loadings are estimated instead of 22), the resulting df is 318 instead of 306. In total 32 params are esimated which should leave 293 df (325-32). Furthermore with 4 latent variables a total of 10 (co)variances are estimated (all factors are inter-correlated). I now have 26 indicators which yield 325 unique pieces of info. Now consider a longitudinal CFA of the same 13 items (2 factors at 2 points in time). This is exactly what I get in my output file (note: Mplus doesn't explicity estimate params for residuals, that's why they're not considered when counting dfs). If I estimate 11 factor loadings (2 items have factor loadings fixed to 1.0 for identification of the 2 latent variables), 2 latent variances & their covariance - 14 total params are estimated leaving model df = 64. The tetrachoric corr matrix has 78 unique pieces of info. I've noticed what appears to be a discrepancy in the computation of df & wondered if someone might spot an error in my reasoning.Ĭonsider a cross-sectional 2 factor model with 13 dichotomous indicators. I'm running a series of CFA models using dichotomous indicators. Mike Willoughby posted on Sunday, J1:56 pm Mplus Discussion > Calculating df when using the WLSM estimator Mplus Home