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RE: st: multiple indirect effects with binary mediators

 From "Muehleck, Kai" To "statalist@hsphsun2.harvard.edu" Subject RE: st: multiple indirect effects with binary mediators Date Wed, 8 May 2013 08:23:23 +0000

```Dear Billy,

thank you for your quick reply! The key variables in my model are:

- gender (IV; female=1),
- number of months on parental leave (MV1; 0 if not on parental leave/no kid),
- holding a management position (MV2; having any subordinates=1) and
- income (DV; natural logarithm of current vocational gross income per hour).

My feeling is that the binary mediator (MV2) is not the main problem (I just realized I mixed up MV1 and MV2 in my previous post; sorry for that). In my view it captures quite well what it is supposed to measure and the distribution is quite ok as well (please let me know if you disagree; 40% of the respondents in the model do have any subordinates [the sample is persons holding an academic degree, that's why the proportion is so high]). Rather my hunch is that the distribution of MV1 is a key problem (75% of all respondents have a value of 0, i.e. have not been on parental leave or have no kid).

What are the problems encountered in Mplus?

Now, there are several technical questions I have but the main problem is: most indirect effects are significant as long as I estimate the model with the maximum number of respondents (N=3720). However, I would also like to analyze subgroups (min. N= 168, max. N=502). Calculating models for the single groups (I have not done a group model yet as too many questions are still pending anyway) mostly yields ns direct and indirect effects. Of course this is related to the smaller N but still the sizes of the effects strike me as pretty big and I think the S.E. are surprisingly large. Effect sizes are reasonably stable in the models for the single groups as compared to the full model but for most variable S.E. increase considerably, sometimes by factor 5. Currently I'm using the WLSMV estimator as this seems the estimator of choice if at least one IV is categorical. I'm asking myself whether there's an alternative to WLSMV with more efficient estimates.

Probably, it is like it is and the model simply doesn't work out for smaller groups. But I would like to check whether an alternative method in Stata (or in Mplus) yields the same results and large S.E. Or I would need to try setting up larger groups (groups are fields of subjects respondents studied) but from a substantial point of view that would clearly be suboptimal.

Any ideas very appreciated!

Best
Kai

HIS Hochschul-Informations-System GmbH
Goseriede 9 | 30159 Hannover | www.his.de
Dr. Kai Mühleck
HIS-Institut für Hochschulforschung
Arbeitsbereich Steuerung, Finanzierung, Evaluation
Internationale Studien & Projektakquise
Telefon +49 (0)511 1220-456 | Fax +49 (0)511 1220-431
E-Mail muehleck@his.de
Registergericht: Amtsgericht Hannover, HRB 6489
Geschäftsführer: Dipl.-Phys. Wolfgang Körner
Vorsitzender des Aufsichtsrats: Prof. Dr. Andreas Geiger

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of William Buchanan
Sent: Tuesday, May 07, 2013 5:52 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: multiple indirect effects with binary mediators

What were the problems you encountered in Mplus?  Maybe they are indicative of a larger underlying problem with fitting the model you are interested in.  Do you know how reliably your binary indicator is measured and whether or not it is derived from some underlying continuous variable?

HTH,
Billy

Sent from my iPhone

On May 7, 2013, at 6:56, "Muehleck, Kai" <Muehleck@his.de> wrote:

> 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
>
>
>
> HIS Hochschul-Informations-System GmbH Goseriede 9 | 30159 Hannover |
> www.his.de Dr. Kai Mühleck HIS-Institut für Hochschulforschung
> Arbeitsbereich Steuerung, Finanzierung, Evaluation Internationale
> Studien & Projektakquise Telefon +49 (0)511 1220-456 | Fax +49 (0)511
> 1220-431 E-Mail muehleck@his.de
> Registergericht: Amtsgericht Hannover, HRB 6489
> Geschäftsführer: Dipl.-Phys. Wolfgang Körner Vorsitzender des
> Aufsichtsrats: Prof. Dr. Andreas Geiger
>
>
>
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