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I am predicting a latent factor from four predictors. How can I estimate the individual contributions (effect sizes) of *two* of the four predictors on the outcome? I'm not sure how to estimate effect sizes in Mplus, but it might be related to rsquared values or the percent of variance in the outcome accounted for by the predictors. Thanks! 


I don't know how one can isolate percent variance contributed by some of a set of predictors when the predictors are correlated. Typically standardized values are used to gauge relative effects. 


Unfortunately, standardized regression coefficients don't determine the relative importance of individual predictors (Willett, Singer, & Martin, 1998, p. 412). Usually, rsquared change (or fsquared) is used to assess effect sizes within the multiple regression framework. Is there a way in Mplus to determine rsquared for an outcome? If so, then presumably I can determine the rsquared without a given predictor, and then calculate the rsquared with the predictor to determine the change in rsquare with the addition of the predictor. Can this be done? Thanks for your help! 


The STANDARDIZED option provides an Rsquare for each dependent variable based on all of its predictors. 

Dan Abner posted on Thursday, December 01, 2011  9:32 am



When I specify the STANDARDIZED option, I obtain both Std and StdYX. I, like Isaac, want a partial Rsquare type of measure of effect size for individual effects. For this purpose, which one (Std or StdYX) should I use? 


I don't think either of those would get you a partial Rsquare. I'm not sure how you would get this. 


Hi Linda, You mentioned earlier that the STANDARDIZED option provides an Rsquare for each dependent variable based on all of the predictors. My SEM includes interactions that are created with the xwith command. Therefore, I believe that only UNSTANDARDIZED output is generated. Is there any way to compute Rsquare using the UNSTANDARDIZED output, or is this not possible? Thank you, Luke 


The following FAQ on the website shows how to compute Rsquare when the model contains an interaction: Latent variable interactions 


Thank you, Linda. 

Anthony posted on Thursday, October 25, 2012  1:44 pm



Hi, I'm conducting a relatively simple path model and I know Mplus provides the total Rsquare value for each outcome variable in the model. However, my reviewers are asking for the variance accounted for by each path. Since each outcome has multiple paths, the Rsquare value doesn't work for this  would it simply be the sum of both the beta (i.e., STDXY estimate) value for the direct effect (none) added to the beta value for the sum of the indirect effects. For instance: Beta1: X1>M1>M2>DV Beta2: X1>M2>DV Beta3: X2>M1>DV Beta4: X2>M2>DV Therefore: Total variance in the DV accounted for by X1 is Beta1+Beta2? Total variance in the DV accounted for by X2 is Beta3+Beta4? Any insights you may be able to provide would be helpful. Thank you! 


Unless the correlations among the x's are zero, these values cannot be obtained. 


I would like to report the total variance explained (rsquared) for my latent factor outcome variable. However, the residual variance of the latent outcome factor was a small, negative, and nonsignificant value. Thus, I fixed the negative residual variance of the latent outcome factor to 0 as recommended from previous posts. Although the model ran, now I do not obtain an rsquared value for my latent outcome factor. Is there any way to circumvent this problem? Thanks! 


When you fix the residual variance to zero, you are in effect saying that Rsquare is one. 

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