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


From   John Antonakis <John.Antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: SEM becomes unidentified when introducing single item control variables
Date   Tue, 15 Jan 2013 19:45:06 +0100

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
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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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