

Significant slope with WLSMV but not ... 

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Jen posted on Wednesday, February 29, 2012  12:07 pm



Hello, I am modeling 8 time points of binary categorical data. Looking at the data, there is no meanlevel change over time (the proportions in order are .092, .131, .098, .101, .091, .072, .094, .114). When I use an MLR estimator, the slope (in a model with only an intercept and a slope; the addition of a quadratic did not improve model fit) is nonsignificant as I would expect (s=.10, p=.28). However, when I use the WLSMV estimator (which would seem to be the better option since it is the default and since fit indices are available), the slope is significant and positive (s=.11, p<.001). The "estimated probabilities" graph with either estimator looks like a flat line (with a slight quadratic curve, which I figure must result somehow from the use of probits/logits, but no increase over time). When I add any covariates to the WLSMV model, the slope is no longer different from 0 (s=.07, p=.29). I am interested in looking at predictors of change (there is variance in the slope), but I would like to report a model without covariates first. The significant positive slope seems very suspicious given the data and the graph. Any thoughts? Thank you! 

Jen posted on Wednesday, February 29, 2012  12:12 pm



Also, I know these estimators treat missing data differently; there is not much missing data and the differing results continue even if I only use complete cases. Thanks again! 


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