

Binary variable and indirect effects ... 

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Dear Dr. Linda Muthen & Dr. Bengt Muthen, I have a path model exploring the associations among social network (two variables: X1 and X2), social support (X3), physical activity (X4), and depression (Y). I am mainly interested in estimating the indirect effects of social network on depression through social support and physical activity:  path 1: X1>X3>X4>Y  path 2: X2>X3>X4>Y Model: Y on X3 X4 X4 on X3 X3 on X1 X2 The problem is, X4 is binary (1 or 0). I read some questions and answers on this discussion board, but I am still not sure what to do. I want to clarify the code to estimate indirect effects as well as direct effects. I would really appreciate your comments on below questions. Q1) Is X4 (physical activity) a predictor or an outcome? My understanding is that it is both. Then, I wonder if I need to use the command “categorical are X4” under Variable? Q2) Do I need to use WLSMV as the estimator to estimate indirect effects and use ML to compare the fitness across models? Q3) Do I need to use Model Constraint instead of Model Indirect if I use WLSMV? Q4) Last, what should I do if X4 is ordinal (e.g., 1=less than 1 day, 2=between 1 and 3 days, 3=between 4 and 6 days, 4=everyday)? 


The simplest approach for you is to Say Categorical = x4 Use Estimator = WLSMV Use Model Indirect 


Thank you so much, Dr. Muthen. Two more questions based on your answers. 1) Do I still use "categorical=x4" when x4 is ordinal? (1234)? 2) Do I use ML if I want to compare the model fitness of the same model, for example, between male and female? 


Yes. No, stay with WLSMV. 


Thank you, again! One more Q. I want to add covariates in my model. I have one continuous (age) and one categorical (obese or not). I wonder what's the difference between 1) controlling for age and obesity in each equation 2) allowing correlations between them and the other variables excluding the dependent variable. (Case 1) Model: Y on X3 X4 age obese X4 on X3 age obese X3 on X1 X2 age obese (Case 2) Model: Y on X3 X4 X4 on X3 X3 on X1 X2 X1 X2 X3 X4 WITH age obese I think we do 1) if age and obese actually have impacts on Y, X4 and X3 and do 2) if I am not sure about the relationships between them and X1X4. Last, In 2), I wonder if it is ok to get the correlation between obese (1/0) and continuous outcome variables? I get a warning message. I would appreciate any advice! Thanks. 


Approach 1 is the way to go. You have 4 covariates (variables not on the lefthand side of ON): x1, x2, age, obese. Covariates should not be on a Categorical list  this list is only for DVs. 


Thank you so much, Dr. Muthen 

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