On 27 March 2011 12:12, jouba <antr...@hotmail.com> wrote: > I am a new user of the function sem in package sem and lavaan for > structural > equation modeling > 1. I dont know what is the difference between this function and CFA > function, I know that cfa for confirmatory analysis but I dont 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 arnt 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]]
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