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Hello Drs. Muthen, I am working with three factor models predicting various outcomes (one at a time), and I would like to compare these models in terms of the observed rsquares for the outcomes (e.g., does one model produce a higher rsquare than another in the same outcome variable). However, my three factor models have different numbers of latent factors, so I would like to make rsquare comparisons among these models using adjusted rsquares. I know that mplus does not provide these values, and I also know that in regression I could convert my rsquare values into adjusted rsquare values using: adjusted rsquare = 1  ( (1Rsq)(N1 / N  k  1) ). My question is: can I use this same formula for my SEM models even though my "k" are latent factors (indicated by many observed categorical variables). So, for example, if my model has 3 latent factors, indicated by 14 observed categorical variables, and my 3 latent factors are all predicting a single categorical outcome variable, does "k" = 3 for the adjusted rsquare formula above? Or is this formula altogether inappropriate for my situation? Thanks, Jim 


Neither Bengt nor I are familiar with the adjusted rsquare. I don't think the issue is whether the variables are latent versus observed. I think the issue would be if you meet the assumptions of this test. 


Thank you for your reply, Can you think of any alternative method (other than comparing adjusted rsquare values) that I could use to compare rsquare values from various dependent variables predicted by multiple factor models? Specifically, say I have two measurement (i.e., factor) models, and I construct several SEM models such that each factor model predicts multiple outcomes one at a time (i.e., so if there are two measurement models and 25 outcomes of interest; there would be 50 SEM models total, each factor model predicting each outcome one at a time). I would like to be able to report which measurement model did a better job of predicting outcomes (i.e., on average, across outcomes), but I can't think of a way to do so. Any guidance would be appreciated. Thanks, Jim 


I'm afraid I don't know of any such method. Maybe someone on SEMNET would have a suggestion. 

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