
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@n4.nabble.com
To: antrael@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 

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