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Hi, I have a quick question regarding indirect effects. I have a model with interval scaled IV and M, but the DV is nominal scaled (binary). Besides odds ratios Mplus provides standardized and unstandardized coefficients for the b path (using MLR). Using model indirect displays an indirect effect based on a*b, however, I am not sure if Mplus already transforms standardized and unstandardized coefficients for b so that model indirect comes to a valid result. If this is not the case, how do I transform coefficients for b appropriately? Thank you very much in advance for your help! Julian 


Not sure what you are asking so let me state a few facts to see if they help. Mplus gives the a*b indirect effect in unstand. and stand. form. The stand. value is the same as the product of a stand. and b stand. With a binary outcome you should use the counterfactuallybased effects. 


Dear Prof. Muthén, thank you for your help. However, I am not very firm with counterfactuals and have problems interpreting the output. Any help would be very welcome! Kind regards Julian TwoTailed Estimate S.E. Est./S.E. PValue Effects from Var1 to Var2 Pure natural DE 0.000 0.000 0.581 0.561 Tot natural IE 0.000 0.000 0.872 0.383 Total effect 0.000 0.000 0.876 0.381 Odds ratios for binary Y Pure natural DE 1.102 0.157 7.039 0.000 Tot natural IE 1.441 0.068 21.329 0.000 Total effect 1.588 0.219 7.240 0.000 Other effects Tot natural DE 0.000 0.000 0.584 0.559 Pure natural IE 0.000 0.000 0.853 0.394 Total effect 0.000 0.000 0.876 0.381 Odds ratios for other effects for binary Y Tot natural DE 1.102 0.157 7.040 0.000 Pure natural IE 1.442 0.068 21.330 0.000 Total effect 1.588 0.219 7.240 0.000 


This is described in our new book at http://www.statmodel.com/Mplus_Book.shtml See also counterfactual papers at http://www.statmodel.com/Mediation.shtml 


Hi, I would like to calculate a path model with 1 continuous IV, 3 continuous mediators (in parallel), 1 sequential continuous mediator and 3 DVs (2 are continuous, 1 is binary). I am using ML estimation (I am thus calculating a logistic regression) and defined the binary outcome as categorical. Using MODEL CONSTRAINT, I calculated all possible indirect effects (BOOTSTRAP = 10000; CINT(bcbootstrap)). To obtain the the correct estimates of the indirect effect involving the binary DV I exponentiated the indirect effects (e.g., ORa1b1 = exp(a1*b1); ORa1b1d1=exp (a1*b1*d1). Is this approach correct? What about direct effect involving the binary DV  is the estimation a B coefficient in the output based on logistic regression? When looking at the output, I also get the odds ratio in case of the direct effect but I don't not the CIs (however, I do get those for these indirect effects). How do I get the CIs of the odds rations for the direct effects? What would be the best way to run the same analyses if the DVs would be ordinal with 4 categories (e.g., which estimator, bcbootstrap or other approaches to calculate the significance of indirect effects)? Many thanks for your help. 


Using ORa1b1 = exp(a1*b1) is only correct assuming that the binary outcome is rare and only for a 1unit change in the continuous x. See Section 8.1.5 in our new book. Estimating pathspecific indirect effects with a binary outcome and several mediators is more complex as mentioned in our book and discussed further in the VanderWeele (2015) book. 

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