Yes, I meant confirmatory factor analysis, I'm sorry if I wasn't clear. I was doing my analyses in lavaan and saw that there are several robust options (MLM, MLMVS, MLMV, MLF, MLR), but I wasn't sure about their specifics, so that was what I was actually asking about. I will definitely consult the links you sent me, thank you!
2016-07-25 11:46 GMT+02:00 Martin Maechler <maech...@stat.math.ethz.ch>: > >>>>> Nika Sušac <nika.su...@gmail.com> > >>>>> on Sat, 23 Jul 2016 19:39:48 +0200 writes: > > > Hi! I have non-normal data (items are continuous on a > > 9-point scale, but not normally distributed) and I want to > > conduct cfa. Which of the estimators available in lavaan > > do you recommend me to use? Thanks in advance! > > I think you want *robust* statistical methods then. > > Robust factor analysis (if 'cfa' means something like that) is > somewhat prominent topic. > Simple approaches will already be available by using > MASS::cov.rob() for a robust covariance matrix which you then > can pass to other methods. > > For more (and more modern) methods and approaches, > > - for R packages, I'd recommend you consult the CRAN task view > about robust statistical methods, > https://cran.r-project.org/web/views/Robust.html > notably the section on 'Multivariate Analysis' > > - for more specific help and expertise, I strongly recommend > the dedicated R-SIG-robust mailing list (instead of R-help), > --> https://stat.ethz.ch/mailman/listinfo/r-sig-robust > > Best regards, > Martin Maechler, ETH Zurich > > > [[alternative HTML version deleted]] > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
