Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

# Re: st: Modeling control variables (covariates) in SEMs: What is the correct approach?

 From Johannes Kotte To statalist@hsphsun2.harvard.edu, Joerg Luedicke Subject Re: st: Modeling control variables (covariates) in SEMs: What is the correct approach? Date Mon, 21 Jan 2013 10:45:36 +0100

```Dear Joerg,

```
thanks for your explanations and corrections. Of course, you are right that MV is a mediator, not a moderator. And you are also right, that approach 2, as I had coded it, does not get me where I want. I am sorry for causing confusion!
```
```
Nevertheless, the question on my mind is whether I have to regress MV and DV on the controls only, or whether the IV has also to be regressed on the controls. For illustration, let me introduce a corrected version of my two approaches (now called A and B)
```

Variables:
IV ...independent variable
MV ...mediator variable
DV ...dependent variable
CV1...control variable 1
CV2...control variable 2

*Approach A* (http://www.ats.ucla.edu/stat/stata/faq/sem_mediation.htm) is
to use the control variables in each of the equations:

sem  (MV <- IV CV1 CV2) (DV <- MV IV CV1 CV2)

```
*Approach B* (used by a fellow researcher I know) is to regress all variables on CV1 and CV2, includig the IV:
```
sem  (IV <- CV1 CV2) (MV <- IV CV1 CV2) (DV <- MV IV CV1 CV2)

Thanks for your help, everybody!

Best
Johannes

Zitat von Joerg Luedicke <joerg.luedicke@gmail.com>:

```
```There seems to be some confusion going on. First, what you call
moderator variable rather looks like a mediator variable to me when
looking at the model you show under "approach 1". Second, when we
regress a on b, it means that we consider a as the outcome and b as
the predictor, not the other way round.

Now as for the modeling strategy, that always depends on what you want
to find out etc. In data analysis, there never is just one solution
for everything. That said, if you were interested in exploring
indirect effects of your main predictor of interest (IV) on your
outcome via the variable you call MV, then your first approach looks
reasonable. From judging without knowing any context here, I would say
that the second approach does not make much sense. Say you have only 3
variables, MV, IV, and DV. If you were interested in regressing DV on
MV while adjusting the effect for IV, then regressing DV and MV on IV
with two separate equations followed up by regressing DV on MV won't
get you there. You simply end up with three separate equations (with
possibly correlated errors) with which you cannot adjust the effect of
MV on DV with IV.

Joerg

On Fri, Jan 18, 2013 at 5:29 AM, Johannes Kotte
<johannes.kotte@st.ovgu.de> wrote:
```
```Hi everybody,

regarding the use of control variables (covariates) in SEMs I have seen two
different approaches and I am not sure which one is the correct one. Can
anybody help me?

Variables:
IV ...independent variable
MV ...moderator variable
DV ...dependent variable
CV1...control variable 1
CV2...control variable 2

*Approach 1* (http://www.ats.ucla.edu/stat/stata/faq/sem_mediation.htm) is
to use the control variables in each of the equations:

sem  (MV <- IV CV1 CV2) (DV <- MV IV CV1 CV2)

*Approach 2* (used by a fellow researcher I know) is to regress CV1 and CV2
on all other variables first and then do the rest of the regressions without
control variables:

sem (DV MV IV <- CV1 CV2) (MV <-IV) (DV<-MV)

To me, this approach looks incorrect. What I find particularly confusing
with this approach is that CV1 and  CV2 are also being regressed on IV.

I am grateful for feedback. Thanks in advance!

One more question comes to my mind: Some of my CVs are correlated at the 5%
level, but their correlation coefficients are below 0.3. Would you exclude
these CVs?

Thanks for your help!
Johannes

--
Johannes Kotte
Otto-von-Guericke-Universität | Fakultät Wirtschaftswissenschaften |
Lehrstuhl für Unternehmensführung und Organisation (Prof. Dr. Thomas
Spengler) | Postfach 4120, 39016 Magdeburg | www.ufo.ovgu.de

E-Mail: johannes.kotte@st.ovgu.de

*
*   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/
```
```
*
*   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/

```
```

--
Johannes Kotte
```
Otto-von-Guericke-Universität | Fakultät Wirtschaftswissenschaften | Lehrstuhl für Unternehmensführung und Organisation (Prof. Dr. Thomas Spengler) | Postfach 4120, 39016 Magdeburg | www.ufo.ovgu.de
```
Telefon: +49-173-6371955  | E-Mail: johannes.kotte@st.ovgu.de

*
*   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/
```