Talk:Structural equation modeling
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Hi, I would like to invite authors to contribute articles to this entry. Some things that can be included are:
1. Lesson plans for teaching SEM 2. List of good books on SEM 3. Key, "must read" articles
Hello,
It seems that emphasizing the difference between linear multiple regression and SEM is important for this article. Does anybody know more about this? To be frankly, I cannot see any urgent needs to state that y_i is determined by y_0,1,2, ..., n (except for i) in SEM. What can be the situations that can be described better with SEM than with linear regression?
I'll be working on this page at my leisure time, if any, as this is my major research topic. What would be the best way to incorporate path diagrams into Wikipedia? I can do matrices with MathML, or whatever interface Wikipedia has for math, but the path diagrams would obviously have to go as graphics files.
Stas Kolenikov
[edit] "SEM has several important advantages ..."
"SEM allows for multiple dependent variable, whereas OLS regressions allows only a single dependent variable." I would say no: SEM is a model where as OLS is an estimation technique. OLS can be used for SEM.
"SEM allows simultaneous tests of multiple groups". I do not understand this: OLS can be used for multivariate regression analysis... In this type of model/analysis there are several response/dependent variables.
"SEM accounts for measurement error, whereas OLS regression assumes perfect measurement." In multivariate regression analysis you have Y=XB+U, and the Y is assumed to be confounded with measurement error (the U's).
- fnielsen 11:24, 10 March 2006 (UTC)
To fnielsen: Most of the notes you made are SEM jargon.
OLS cannot be used for SEM because of the measurement error. Or rather the class of SEMs to which OLS is applicable is very narrow (recursive/trinagluar models where all variables are observed; those are not very interesting).
Multiple groups means that you might have different parameters for different subpopulations. In OLS, you would model that through interactions. In SEM, however, there are way more parameters than just slopes, so you may have say the same loadings, but different measurement error variances between males and females. Multiple group comparisons are then based on nested hypotheses where you allow some of the model parameters vary between groups.
OLS assumes X's are fixed. Typically, that's too much a luxury to assume with SEM where most of the observed variables are truly random variables and contain measurement error.
GLM means generalized linear model to most statisticians; using it for general linear model is rather awkward, I'd say.
- Stas Kolenikov 16 March 2006
