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crystal posted on Sunday, December 07, 2014  12:45 pm



Hi I am trying to create a SEM model with a quadratic effect between my mediating variable and my outcome variable (X1, X2 AND X3) all predict mediating variable and the mediating variable predicts Y1 X1 X2 mediating variable  Y1 X3 i want to model a quadratic relationship between mediating variable and y1. do i just add a power term of mediating variable (squared) and have it predict Y1? Also, do i need my exogenous variables predicting the squared term? 


Create m2 in Define: Define: m2 = (mmbar)*(mmbar); !mbar = mean Then use Model: y on m m2; m on x1x3; Here, m2 will be a covariate and automatically correlated with x1x3. Because of the nonlinearity I would ignore the chisquare testing. 


Also, the indirect effect estimation is not straightforward in this case. Perhaps it could be derived from my paper on our website: Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. 


Dear Mplus team, I got huge lessons from your suggestion on the issue of handing the meditation model with a quadratic factor in the section "nonlinear factor analysis" The model depiction follows (variables are latent and continuous except for outcome which is continuous but observed). Y on X, Xsquared, M Y IND X; Y IND X M; The syntax with not this one. This describes what the model looks like. However, I thought that the meaning of the coefficient of (X) in the model which quadratic term included is different from a model without the quadratic term (Tradition linear mediation model), due to the fact that the coefficient (c1b1+c2) represents the slope at X=0 rather than the slope of whole data. A brief description of the quations of my model M = b0 + b1(X)  (1) Y = c0 + c1(M) + c2(X) + c3 (Xsquared)  (2) Y = c0 + b0c1 + (c1b1+c2)(X) + c3(Xsquared)  (3) Hence, I am asking that if I can't express a linearly indirect effect and curvilinear relationships between X and Y at the same time. I found out a paper discussing a quadratic indirect effect but not in the case of my mine. Thank you to respond to my question. 


I don't understand how you can have both equations (2) and (3) in the same model. The model of (1) and (2) is expressed as m on x; y on m x x2; where x2 is defined as x squared using XWITH. 

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