From: jouba <antrael_at_hotmail.com>

Date: Sun, 27 Mar 2011 19:18:56 -0700 (PDT)

Date: Sun, 27 Mar 2011 19:18:56 -0700 (PDT)

Jeremy thanks a lot for your response

I have read sem package help and I currently reading the help of lavaan
I see that there is also an other function called lavaan can do the SEM analysis
So I wonder what is the difference between this function and the sem function
Also I am wondering in the case where we have categorical variables and discreet variables??
For me one of the problems is how we will calculate the correlation matrix , mainly when we have to calculate these between a quantitative and qualitative variables, I wonder if polycor package is the best solution for this or there is other ideas for functions witch can do the work
Cordially

Antra EL MOUSSELLY

Date: Sun, 27 Mar 2011 18:08:02 -0700

From: ml-node+3410447-849581659-225466_at_n4.nabble.com
To: antrael_at_hotmail.com

Subject: Re: Structural equation modeling in R(lavaan,sem)

On 27 March 2011 12:12, jouba <[hidden email]> wrote:

> I am a new user of the function sem in package sem and lavaan for

*> structural
**> equation modeling
**> 1. I don’t know what is the difference between this function and CFA
**> function, I know that cfa for confirmatory analysis but I don’t know what
**> is the difference between confirmatory analysis and structural equation
**> modeling in the package lavaan.
**>
*

Confirmatory factor analyses are a class of SEMs. All CFAs are SEMs, some SEMs are CFA. Usually (but definitions vary), if you have a measurement model only, that's a CFA. If you have a structural model too, that's SEM.

If you don't understand this distinction, might I suggest a little more reading before you launch into the world of lavaan? Things can get quite tricky quite quickly.

> 2. I have data that I want to analyse but I have some missing data I must

*> to
**> impute these missing data and I use this package or there is a method that
**> can handle missing data (I want to avoid to delete observations where I
**> have
**> some missing data)
**>
*

No, you can use full information maximum likelihood estimation (= direct ML) to model data in the presence of missing data.

> 3. I have to use variables that arn’t normally distributed , even if I

*> tried
**> to do some transformation to theses variables t I cant success to have
**> normally distributed data , so I decide to work with these data non
**> normally distributed, my question my result will be ok even if I have non
**> normally distributd data.
**>
*

Depends. Lavaan can do things like Satorra-Bentler scaled chi-square, which are robust to non-normality, and corrects your chi-square for (multivariate) kurtosis.

> 4. If I work with the package ggm for separation d , without latent

*> variables we will have the same result as SEM function I guess
**>
*

Not familiar with ggm. I'll leave that for someone else.

> 5. How about when we have the number of observation is small n, and what

*> is
**> the method to know that we have the minimum of observation required??
**>
**>
**>
**>
*

Another very difficult question. Short answer: it depends. Sometimes you
see recommendations based on the number of participants per parameter, which
is usually around 5-10. These are somewhat flawed, but it's better than
nothing.

Again, I should reiterate that you have a hard road in front of you, and it will be made much easier if you read a couple of introductory SEM texts, which will answer this sort of question.

Jeremy

-- Jeremy Miles Psychology Research Methods Wiki: www.researchmethodsinpsychology.com [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. If you reply to this email, your message will be added to the discussion below:http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3410447.html To unsubscribe from Structural equation modeling in R(lavaan,sem), click here. -- View this message in context: http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3410587.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]Received on Mon 28 Mar 2011 - 03:22:00 GMT______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.

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