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Re: st: SEM becomes unidentified when introducing single item control variables
From
Johannes Kotte <[email protected]>
To
[email protected], Alan Acock <[email protected]>
Subject
Re: st: SEM becomes unidentified when introducing single item control variables
Date
Tue, 15 Jan 2013 21:17:33 +0100
Hi Alan,
thanks a lot for your explanation! You addressed exactly the two
questions that remained after the last answer from John.
And thanks for writing the book "A gentle introduction to Stata". It
was a survival guide for me as a Stata Novice :-)
Best
Johannes
Zitat von Alan Acock <[email protected]>:
Johannes,
You could use
.sem (x1<- X1), reliability(x1 .8)
Then, you could try other estimates of reliability to do a
sensitivity analysis. If you assume there is no measurement error,
then you would simply use x1 as is and not use a latent variable for
it.
Alan Acock
On Jan 15, 2013, at 10:45 AM, Johannes Kotte
<[email protected]> wrote:
Hi Billy,
makes complete sense what you say about the covariates - thanks for
your help!
What I meant by "I have already seen models with latent single-item
variables" is that some authors use single-item latent variables
isntead of the observed ones (like I tried to). What I don't
understand is how this can work, considering my experience that
latent single-item variables cannot be identified.
Best
Johannes
Zitat von William Buchanan <[email protected]>:
Hi Johannes,
I'm not sure why you would use several latent variables for observed
covariates. If you wanted a measurement model for your covariates it would
be something more like:
(x16 x17 x18 x19 <- Covariates)
But given what you've mentioned about the variables, it doesn't seem like
this would be a good idea (e.g., suggesting that some unobservable variable
affects someone's gender, age, and what I presume would be other
demographic
indicators). Why is it not acceptable to include your observed
variables as
covariates? If you're going to mention how you've seen this done before in
other articles/papers it would also be a good idea to reference
those papers
so others can approach helping you from the same frame of reference. And
you should include the output from your command(s) as well as the syntax
that you've used to produce them. Sometimes you may have just overlooked a
small, but important, piece of information that could explain a lot of the
problems you're running into.
HTH,
Billy
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Johannes Kotte
Sent: Tuesday, January 15, 2013 8:50 AM
To: [email protected]; JVerkuilen (Gmail)
Subject: Re: st: SEM becomes unidentified when introducing single item
control variables
Thanks for your reply!
I looked at the model identification after letting sem iterate for a few
times. The df are above 60, so I always thought that identification is no
issue.
Now this might sound stupid, but I always thought that "(x16 <- CV1) ...
(x19 <- CV4)" IS my measurement model for the control variables.
However, you are right that CV1-CV4 are unidentified if I run the
measurement models alone. As they are single-item variables like gender,
age, etc., I (obviously wrongly) presumed that they cannot be unidentified.
Nevertheless, they don't have to be latent (I guess), even though I have
already seen models with latent single-item variables. So, if I altered
model 2 as follows (with x16 x17 x18 x19 being the controls), would that be
correct?
sem (y1 y2 y3 y4 <- PRAXREL) ///
(x1 x2 x3 x4 x5 x6 x7 <- BKA) ///
(x8 x9 x10 x11 <- KVSENIOR) ///
(x12 x13 x14 x15 <- KVL) ///
(BKA PRAXREL <- KVSENIOR KVL x16 x17 x18 x19) ///
(PRAXREL <- BKA) ///
, standardized method(mlmv)
I tried the above sem and it works. However, the estat mindices command
results in missing values only, even for the latent constructs
Again, thanks a lot!
Johannes
--------------------------------------- Original e-mail
---------------------------------------
Zitat von "JVerkuilen (Gmail)" <[email protected]>:
The standard errors being crazy is a sign that the model is not
identified. I'd suspect it's because the latent variables for these
controls aren't identified, and given that it doesn't sound like you
have a measurement model for them I'm not sure how they could be. Why
are they latent anyway?
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----------------------------------------------------------------------
Datum: Tue, 15 Jan 2013 15:21:36 +0100
Von: Johannes Kotte <[email protected]>
Betreff: SEM becomes unidentified when introducing single item control
variables
An: [email protected]
Dear fellow researchers,
I would be grateful for advice with the following problem: I have created a
very simple SEM (let's call it 'model 1') that works fine (see below for
code). It contains a latent dependent variable called PRAXREL and a latent
independent variable called BKA. Moreover, it contains latent control
variables called KVSENIOR and KVL. As I said, model 1 works fine
(identified, good fit).
However, the model becomes problematic when I introduce single-item latent
variables (CV1, CV2, CV3, CV4) as control variables ('model2').
In this case Stata iterates forever saying «not concave».
WHAT COULD BE THE REASON? I tried many different setups of the model (incl.
constraining the path coefficients of the CV to 1 or setting the
reliability
of the CV to 0.9 or 0.5) but none of them really worked unless I delete at
least some of the CVs.
The following might be interesting: (i) If I let Stata iterate 15 times and
take a look at the output, I find that sometimes the standard
errors of CV1,
CV2, CV3 and CV4 are extremely high. (ii) Moreover, I found that pairwise
correlation of the variables shows that they are mostly correlated - at
least at the 10% level, sometimes even 1%. Might there be a collinearity
problem?
Can anybody give me advice? I would greatly appreciate that!
Thanks in advance!
Johannes
CODE FOR BOTH MODELS:
/***MODEL 1***/
sem (y1 y2 y3 y4 <- PRAXREL) ///
(x1 x2 x3 x4 x5 x6 x7 <- BKA) ///
(x8 x9 x10 x11 <- KVSENIOR) ///
(x12 x13 x14 x15 <- KVL) ///
(BKA PRAXREL <- KVSENIOR KVL) ///
(PRAXREL <- BKA) ///
, standardized method(mlmv)
/***MODEL 2***/
sem (y1 y2 y3 y4 <- PRAXREL) ///
(x1 x2 x3 x4 x5 x6 x7 <- BKA) ///
(x8 x9 x10 x11 <- KVSENIOR) ///
(x12 x13 x14 x15 <- KVL) ///
(x16 <- CV1) ///
(x17 <- CV2) ///
(x18 <- CV3) ///
(x19 <- CV4) ///
(BKA PRAXREL <- KVSENIOR KVL CV1 CV2 CV3 CV4) ///
(PRAXREL <- BKA) ///
, standardized method(mlmv)
--
Johannes Kotte
Otto-von-Guericke-Universität | Faculty of Business and Economics| Chair of
Management and Organization (Prof. Thomas Spengler) | Postfach 4120, 39016
Magdeburg | www.ufo.ovgu.de
Telefon: +49-173-6371955 | E-Mail: [email protected]
*
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*
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--
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: [email protected]
*
* For searches and help try:
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* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/
*
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* 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/