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From |
"William Buchanan" <william@williambuchanan.net> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: SEM becomes unidentified when introducing single item control variables |

Date |
Tue, 15 Jan 2013 09:27:28 -0800 |

Hi Johannes, I'm not sure why you would use several latent variables for observed covariates. If you wanted a measurement model for your covariates it would be something more like: (x16 x17 x18 x19 <- Covariates) But given what you've mentioned about the variables, it doesn't seem like this would be a good idea (e.g., suggesting that some unobservable variable affects someone's gender, age, and what I presume would be other demographic indicators). Why is it not acceptable to include your observed variables as covariates? If you're going to mention how you've seen this done before in other articles/papers it would also be a good idea to reference those papers so others can approach helping you from the same frame of reference. And you should include the output from your command(s) as well as the syntax that you've used to produce them. Sometimes you may have just overlooked a small, but important, piece of information that could explain a lot of the problems you're running into. HTH, Billy -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Johannes Kotte Sent: Tuesday, January 15, 2013 8:50 AM To: statalist@hsphsun2.harvard.edu; JVerkuilen (Gmail) Subject: Re: st: SEM becomes unidentified when introducing single item control variables 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? > * > * 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/ ---------------------------------------------------------------------- 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 * * 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/

**Follow-Ups**:**RE: st: SEM becomes unidentified when introducing single item control variables***From:*Johannes Kotte <johannes.kotte@st.ovgu.de>

**References**:**st: SEM becomes unidentified when introducing single item control variables***From:*Johannes Kotte <johannes.kotte@st.ovgu.de>

**Re: st: SEM becomes unidentified when introducing single item control variables***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**Re: st: SEM becomes unidentified when introducing single item control variables***From:*Johannes Kotte <johannes.kotte@st.ovgu.de>

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