R help - Mar 2011 - Structural equation modeling in R(lavaan,sem)

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. 
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)
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.
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
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??



Thanks a lot 




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On 27 March 2011 12:12, jouba <antrael@hotmail.com> 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]]

Hi,

Your question is so broad as to be unanswerable, but see the help
pages for the function from both packages.  Here is how you can load
them both and then look at the help for a specific package:

require(sem)
require(lavaan)

help("sem", package = "sem")
help("sem", package = "lavaan")

If a particular aspect of their implementation or use is confusing,
feel free to ask.

Cheers,

Josh

On Sun, Nov 6, 2011 at 9:29 PM, loyolite270 <loyolite270 at gmail.com>
wrote:
> I am new to both sem and lavaan package ...
>
> I dint exactly get the difference between sem from sem package and sem from
> lavaan package... ,
>
>
>
> --
> View this message in context:
http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3997527.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>


-- 
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, ATS Statistical Consulting Group
University of California, Los Angeles
https://joshuawiley.com/