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

[Johannes Kotte <johannes.kotte@st.ovgu.de>]

Hi John,

magnificent, this helps a lot! I already tried setting the constraint  
@1 and to use the reliability-option but never used the two together.

However, two questions remain:

(i) Previous answers said that I could simply include the observed  
variables instead of using latent covariates. Which approach would be  
more appropriate? (any literature on that?)

(ii) Lets's assume that "(x16 <- CV1@1)" defines CV1 as the latent  
variable for gender (x16). Why would I set the reliability to anything  
below 1.0, if x16 is perfectly reliable (which is a reasonable  
assumption, I guess)?

Best
Johannes


For those who are interested in this thread, my model now looks like  
the following:

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@1) ///
(x17 <- CV2@1) ///
(x18 <- CV3@1) ///
(x19 <- CV4@1) ///
(BKA PRAXREL <- KVSENIOR KVL CV1 CV2 CV3 CV4) ///
(PRAXREL <- BKA) ///
, standardized method(mlmv) reliability (x16 0.8 x17 0.8 x18 0.8 x19 0.8)


Zitat von John Antonakis <John.Antonakis@unil.ch>:

> Hi:
>
> The model is undefined. You need to set constraints linking the  
> single indicator (e.g,. x1) of the latent (X), as follows:
>
> sem (y <- X) (X ->x1@1), reliability(x1 .80)
>
> Where reliability < 1 > 0, is your theoretical constraint of how  
> much true variance x1 captures.
>
> See "help sem reliability"
>
> If course, if you set x1 = 1 you are assuming that x1 is perfect  
> indicator of X.
>
> HTH,
> J.
>
> __________________________________________
>
> Prof. John Antonakis
> Faculty of Business and Economics
> Department of Organizational Behavior
> University of Lausanne
> Internef #618
> CH-1015 Lausanne-Dorigny
> Switzerland
> Tel ++41 (0)21 692-3438
> Fax ++41 (0)21 692-3305
> http://www.hec.unil.ch/people/jantonakis
>
> Associate Editor
> The Leadership Quarterly
> __________________________________________
>
> On 15.01.2013 15:21, Johannes Kotte wrote:
> > 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)
>
> *
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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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