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


From   Johannes Kotte <johannes.kotte@st.ovgu.de>
To   statalist@hsphsun2.harvard.edu, "JVerkuilen (Gmail)" <jvverkuilen@gmail.com>
Subject   Re: st: SEM becomes unidentified when introducing single item control variables
Date   Tue, 15 Jan 2013 17:49:39 +0100

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?
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----------------------------------------------------------------------
     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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