re: st: multiple indirect effects with binary mediators

["Ariel Linden, DrPH" <ariel.linden@gmail.com>]

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



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