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| 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 22:04:35 +0100 |
Glad it works.You only need to model as latent whatever is not perfectly observed. Gender should be. Age might not (people may misreport that). Psychological constructs and attitudes are certainly measured with error.
Best, 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 21:11, Johannes Kotte wrote:
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 JohannesFor 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)* * 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/
* * 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/