Heywood case for LATENT variables

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Jie Ren

May 26, 2018, 10:12:00 AM5/26/18
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I am currently examining whether language learning is correlated with inhibitory control for 2-year-old toddlers. I treated language and inhibitory control as two latent variables and there are four different measurements for each of the two latent variables. I ran the following model using lavaan, but the model ends up with a negative variance value for one of the LATENT variables SWITCH which measures Inhibitory control (of course the program sent a warning saying that some estimated ov variances are negative). 

LANGUAGE=~a*WordComp + b*WordProd + c*SentComp+ d*SentProd

As you can see, the model is really not a huge one. However, I am really a novice in SEM though I have received formal statistical training with a master's degree in Biostatistics. I wonder whether I could learn from your insight about this. What could have happened so that the estimated variance is negative? Any suggestion about how to solve this problem would be appreciated. 

Stas Kolenikov

May 26, 2018, 3:18:14 PM5/26/18
to lav...@googlegroups.com
Ken Bollen and I published a paper on Heywood cases a few years ago: http://journals.sagepub.com/doi/10.1177/0049124112442138

-- Stas Kolenikov, PhD, PStat (ASA, SSC)  @StatStas
-- Senior Scientist, Abt Associates @AbtDataScience
-- Program Chair (2018), Survey Research Methods Section of the American Statistical Association
-- Opinions stated in this email are mine only, and do not reflect the position of my employer
-- http://stas.kolenikov.name

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