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Indirect Model in mediation analysis |
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I have never done a mediation analysis before, and I have also never used Mplus before, so I could use some guidance! My code is currently structured as follows: VARIABLE: NAMES are (I list many variables here)... USEVARIABLES are x1 x2 x3 y m; CATEGORICAL are y m; ANALYSIS: bootstrap=5000; MODEL: m ON x1 x2 x3; y ON x1 x2 x3 m; MODEL INDIRECT: y IND m; OUTPUT: sampstat interval(bootstrap); x is a 4 level categorical variable dummy coded to 3 levels, m is binary, and y is a categorical variable with 3 levels. The code runs fine with no warnings, but I am concerned because for the indirect effects, the estimates are: Estimate SE Est./SE p-value 0.000 0.000 999.000 0.000 Any advice you can offer would be greatly appreciated! |
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Take a look at the Model Indirect topic described in the V8 UG on or website. A common form is to say Y IND m x; Your input is lacking the x. To understand the mediation topic well in the context of categorical y and m, read our book Regression and Mediation Analysis using Mplus, especially chapter 8 which has several examples (Mplus scripts are on our website). |
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Thank you for your help! Our team has realized that our outcome needs to be treated as nominal, but if we use the NOMINAL= option, we cannot get indirect effects. Can you tell me if there is a way to code this? Also, so far we are not seeing a very large mediation effect and we are wondering if there are any guidelines as to how large an effect size should be in order to determine if a mediation analysis is the right way to go on this project. |
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So you are saying that y is not ordinal but nominal? That complicates matters. One way is to dichotomize y in the different ways possible and run one mediation analysis at a time. If you have not done mediation analysis before you want to read up on it. Regarding your last question, try asking on SEMNET. |
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