I am trying to use the cfa command in the lavaan package to run a CFA
however I am unsure over a couple of issues.
I have @25 dichotomous variables, 300 observations and an EFA on a
training dataset suggests a 3 factor model.
That is a lot of variables, and a rather small sample size (for binary
data).
After defining the model I use the command
fit.dat <- cfa(model.1, data=my.dat, std.lv = T, estimator="WLSMV",
ordered=c("var1","var2" and so on for the other 23 variables))
To avoid having to type "var?" 25 times, you can say
ordered=paste("var",1:25,sep="")
Is it right that I define the variables as ordered (the output
returns thresholds suggesting I should).
Yes!
Does the cfa command
calculate tetrachoric correlations in the background?
Yes, indeed. You can 'see' it by typing
inspect(fit, "sampstat")
lavaan also computes an asymptotic variance matrix of these
correlations, so you should get correct standard errors and a correct
test statistic. By default, lavaan will provide robust standard errors
and a mean and variance adjusted test statistic (estimator="WLSMV").
However, output for the command returns two variables with small
negative variances (-0.002) which I think is due to the correlation
matrix not being positive definite. Is it reasonable to force these
to be zero when defining the model or is this more a sign of problems
with the model?
You can NOT force these to be equal (at least not in the current version
of lavaan - 0.5-11, where the residual variance is a function of other
model parameters). I don't think this is caused by a non-pd correlation
matrix (you should get a big warning if this was the case). Perhaps the
sample size is too small. Could you remove some items, or regroup them?
As an alternative is it possible to calculate the tetrachoric
correlations using hetcor (which applies smoothing) and then use the
smoothed sample correlation as the input to the model, such as
fit.cor <- cfa(model.1, sample.cov=my.hetcor, sample.nobs=300, std.lv
= T,estimator="ML", ordered=c("var1","var2" and so on for the other
23 variables)).
This will work only if you omit the 'ordered' argument. Perhaps in
combination with estimator="ULS". But do not trust/report the standard
errors in this case.
Final question is I have a lot of missing data - listwise deletion
leaves 90 subjects. Is there a way to calculate estimates using
pairwise deletion (this is another reason why I tried using the
correlation matrix as the input).
You could do this, and use estimator="ULS". But again, you can not use
the standard errors.
Yves.
--
http://lavaan.org
______________________________________________
R-help@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.
