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

[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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