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

From: jouba <antrael_at_hotmail.com>
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 

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