Elina Dale posted on Saturday, October 19, 2013 - 9:16 am
Dear Dr. Muthen,
I've just realized that unlike with regression models where we have observed predictors and outcomes, in SEM I do not know exactly how to check the assumptions such as linearity of relationship between factors (predictors in this case) and an observed outcome.
I am modeling motivation factors as predictors of intention to stay (measured as observed continuous variable). I am not sure how to check in MPlus after I run my model whether assumptions of the model hold. I can see global fit indices but they don't show how well our assumptions (including linearity) hold, do they?
Thank you very much for your guidance and help!!!!
You have to check this using individual residual plots using the Scatterplot option in the plot menu. Because this works with raw data, you have to do it separately for each imputed data set. For individual residuals, see the paper on our website:
Asparouhov, T. & Muthén, B. (2014). Using Mplus individual residual plots for diagnostic and model evaluation in SEM. Web note 20.
Jinxin ZHU posted on Monday, May 11, 2015 - 1:20 am
Thank you very much for your previous help.
Now I am doing a multilevel path analysis. Random intercepts were modeled for all dependent variables (say A, B, and C). At level-one, C on A B and other predictors, and B on A and other predictors. At level-two, only mean and variances were estimated.
1. When I use the individual residual plots, there are some variables begin with B_. What are these variables?
2. Are the residuals differed cross different levels? Say level one residual and level two residual if a path was specified at level two?
3. Only a general variable of residual was estimated for each dependent variable. Would you please kindly suggest the way how to conduct the regression diagnosis for multilevel path analysis using Mplus?
1. The estimate for the between part of the variable aka random intercept estimate. You can save these with "savedata: file=1.dat; SAVE = FSCORES;"
2. The residual in the Mplus plot is C-E(C|"predictor variables"). The "predictor variables" are the variables that are not dependent variables (are not modeled, i.e. no variance or intercept are reported for those variables). In principle you can construct the residual in different ways depending on what variables you condition on and you can also look at within and between residuals separately but currently Mplus will give you this C-E(C|"predictor variables"). You can use the savedata option to save the estimates and form alternative residuals for example in excel.