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From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
re: st: multiple indirect effects with binary mediators |

Date |
Wed, 8 May 2013 11:25:44 -0400 |

It seems to me that you will have to rescale the variables in order to have the results on the same scale across variable types. See the following references that speak to this: Winship, C., Mare, R.D.: Structural equations and path analysis for discrete data. American Journal of Sociology 89, 54-110 (1983) Winship, C., Mare, R.D.: Regression models with ordinal variables. American Sociological Review 49, 512-525 (1984) Wooldridge, J.M.: Econometric Analysis of Cross Section and Panel Data. MIT Press, Cambridge, MA (2002) Cramer, J.S.: Logit Models. From Economics and Other Fields. Cambridge University Press, Cambridge (2003) MacKinnon, D.P., Dwyer, J.H.: Estimation of mediated effects in prevention studies. Evaluation Review 17, 144-58 (1993) Karlson, K.B., Holm, A.: Decomposing primary and secondary effects: A new decomposition method. Research in Stratification and Social Mobility 29, 221-237 (2011) Karlson, K.B., Holm, A., Breen. R.: Comparing regression coefficients between models using logit and probit: A new method. Sociological Methodology 42, 274-301 (2012) Kohler, U., Karlson, K.B., Holm A.: Comparing coefficients of nested nonlinear probability models. The Stata Journal 11, 420-438 (2011) Karlson, Holm, and Breen; 2012; Karlson and Holm 2011; Kohler, Karlson, and Holm 2011 Date: Tue, 7 May 2013 13:56:41 +0000 From: "Muehleck, Kai" <Muehleck@his.de> Subject: st: multiple indirect effects with binary mediators Dear all, I would like to estimate the direct indirect effects of an independent variable. Some of the mediating variables are binary, some are continuous. The dependent variable is continuous (natural logarithm of income). There are some packages allowing for binary mediators (medeff, ldecomp), however as I understand none of them allow for specifying multiple indirect effects in the sense of subsequent mediating variables. What do I mean by that? Theoretical considerations suggest that one group of mediators influences the other group of mediators. Thus the MVs influence the DV subsequently: IV -> MV1 -> MV2 -> DV. In this model the total effect of the IV would be made up of the direct effect (IV -> DV) and three indirect effects: (1) IV -> MV1 -> DV; (2) IV -> MV2 -> DV; (3) IV -> MV1 -> MV2 -> DV; MV1 is binary. MV2 is continous (but far from normally distributed with 75% of all respondents on value 0 and the rest distributed across a long positively skewed tail). The DV is continous as well and close to normally distributed. My questions are: (1) How can I estimate the direct and indirect effects of the IV? (2) How can I estimate the different indirect effects of MV1 and MV2 and their total effects? Obviously this asks for some kind of path analysis. However, Stata's sem command only allows for continuous DVs (and continuous MVs I suppose). I have also tried to do this using Mplus but ran into several other problems I couldn't yet solve. Thus I'm again trying to explore Stata's potential to do such an analysis. Any help and hints highly appreciated! Best regards, Kai * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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