

SEM regression coefficient 

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Dear Sir or Madame, I´ve run an EFA followed by a CFA for three questionnaires. The observed variables were treated as ordered categorical and I used the WLSMVestimator. It turned out, each of the 3 questionnaire consits of 3 factors. I´ve run a SEM with all three questionnaires using the following model command: A1 on B1 B2 B3; A1 on C1 C2 C3; A2 on B1 B2 B3; A2 on C1 C2 C3; A3 on B1 B2 B3; A3 on C1 C2 C3; output: standardized (stdyx); For example, for the Factors A1, B1 and C1 the results were (STDYX Estimates): B1 WITH C1 0.9 A1 ON B1 0.65 A1 ON C1 0.75 B1 and C1 are highly correlated. I would like to know how much they overlap in their prediction on A1. My question is: What do the regression coefficients ( 0.65 and 0.75) exactly mean? Is the regression coeffizient of B1 on A1 only based on B1? Are the effects of the other B2,C1,.. partialed out? I hope you can understand my question, despite of my bad english. Thank you very much for your help! 


Hi, i´m sorry, but I have to correct myself. My question is: Is the Regression coefficient of A1 on B1 ( A1 as the idepentend factor, and not the othder way around) based on a partial regression? 


When you say a1 ON b1; a1 is the dependent variable and b1 is the independent variable. If you have a1 ON b1 c1; the regression coefficients for b1 and c1 are partial regression coefficients. 


Hi, thank you very much for the quick answer! This ist my model command: A1 on B1 B2 B3; A1 on C1 C2 C3; A2 on B1 B2 B3; A2 on C1 C2 C3; A3 on B1 B2 B3; A3 on C1 C2 C3; output: standardized (stdyx); A1A3 are the dependend variables, B1B3 and C1C3 are the independet variables, I mixed that up in my last post. If i got you right, all coefficients are partial regression coefficients and it doesn´t matter that I´ve six OnStatemants, right? Is it possible, given all the partial regression coefficients and the correlations between B (13) and C(13), to answer the question how much B1B3 overlaps with C1C3 in their prediction on A, or will I have to run further analyses in order to answer that question? Thank you very much for your help! 


I don't think what you want is possible because the covariates are correlated. You might want to get the opinions of others on SEMNET. 

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