Re: st: SEM becomes unidentified when introducing single item control variables

[Alan Acock <acock@me.com>]

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 <johannes.kotte@st.ovgu.de> 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 <william@williambuchanan.net>:
> 
>> 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: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Johannes Kotte
>> Sent: Tuesday, January 15, 2013 8:50 AM
>> To: statalist@hsphsun2.harvard.edu; 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)" <jvverkuilen@gmail.com>:
>> 
>>> 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?
>>> *
>>> *   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/
>> 
>> ----------------------------------------------------------------------
>>      Datum: Tue, 15 Jan 2013 15:21:36 +0100
>>        Von: Johannes Kotte <johannes.kotte@st.ovgu.de>
>>    Betreff: SEM becomes unidentified when introducing single item control
>> variables
>>         An: statalist@hsphsun2.harvard.edu
>> 
>> 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: johannes.kotte@st.ovgu.de
>> 
>> 
>> *
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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: johannes.kotte@st.ovgu.de
> 
> 
> *
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