We have a client who would like to use this technique to do analysis. Before we can recommend it or not, we would like to learn about it. Does MPlus do it? Has anyone ever heard of it? Thanks.
bmuthen posted on Thursday, June 28, 2001 - 1:19 am
No Mplus does not do Partial Least Squares. PLS is a technique championed by Herman Wold as an alternative to the LISREL ML approach by Joreskog (Wold's student). There is a literature on PLS - early writings include a 1982 North Holland 2-part volume called Systems Under Indirect Observation (Joreskog & Wold eds.). SEMNET members probably can be asked about more recent contributions.
Anonymous posted on Sunday, March 06, 2005 - 4:42 am
If M-plus Version 3.11 connot handle PLS estimation what estimation method is recomended for SEM models with non-normal data (high skewed data)?
We have a few estimators that are robust to non-normality. I would recommend what we refer to as MLR. With this estimate, you obtain maximum likelihood parameter estimates with standard errors and a chi-square test statistic that is robust to non-normality. The standard errors are computed using a sandwich estimator. The chi-square test statistic is also referred to as the Yuan-Bentler T2 test statistic.
If your variables are censored, Mplus also has an option for dealing with censored data.
Since this thread seems to have ended quite some time ago, I was wondering if the current version of MPlus is capable of fitting path analysis models with latent variables? More specifically, I had someone ask me about fitting a path model that includes a single latent variable (Youth Human Capital in their words), a mediator (program participation), and several covariates (i.e., race, native language, etc...). Is is possible to fit models like this now?