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?
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, X-squared, 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 (X-squared) -- (2) Y = c0 + b0c1 + (c1b1+c2)(X) + c3(X-squared) -- (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.