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Last updated: 2020-05-06

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Knit directory: mcfa-para-est/

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File	Version	Author	Date	Message
html	51afcfa	noah-padgett	2020-03-31	Build site.
Rmd	f3a547f	noah-padgett	2020-03-31	fixed reference link
html	f3a547f	noah-padgett	2020-03-31	fixed reference link
Rmd	85e8fd8	noah-padgett	2020-03-31	updated bias pages
html	e17f2b3	noah-padgett	2020-03-31	Build site.
Rmd	47117a0	noah-padgett	2020-03-31	updated bias pages
html	47117a0	noah-padgett	2020-03-31	updated bias pages
html	35a72f4	noah-padgett	2020-03-30	initial commit
Rmd	25ca73e	noah-padgett	2020-03-30	initial commit
Rmd	dab7ac9	noah-padgett	2019-06-14	Start workflowr project.

Welcome to our research website on multilevel CFA. On this site, you will find the results of our study on parameter recovery across robust estimation methods for multilevel CFA. We utilizes three different estimators:

MLR: maximum likelihood with robust standard errors
ULSMV: unweighted least squares with mean and variance adjusted \(\chi^2\)
WLSMV: weighted least squares with mean and variance adjusted \(\chi^2\)

This project is additional results from R. Noah Padgett’s master’s thesis project. The simulation project was quite extensive and mainly focused on fit statistics. This project extends that work by using the a subset of the output files to report on parameter recovery.