Last updated: 2020-06-10

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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:

  1. MLR: maximum likelihood with robust standard errors
  2. ULSMV: unweighted least squares with mean and variance adjusted \(\chi^2\)
  3. 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.

Admissible Replications

*Admissible Replications and Convergence

Next, identify the proportion of replications where ULSMV converged and admissible when WLSMV was not. Maybe you can make like a set of 2x2 tables of that show this relationship as sample size decreases.

Parameter Estimates

*Overview of Methods

Effect of Design Factors

The following pages investigated the effect of the simulation design (sample size, parameters, estimation) on the estimates for the different parameters and the estimated level of bias (relative bias). The final results are reported as effect sizes (e.g., omega-squared or partial-omega-squared).

*Design Effects on Parameter Estimates

*Design Effects on Relative Bias Estimates

Standard Errors

*Overview of Methods

Effect of Design Factors

The following pages investigated the effect of the simulation design (sample size, parameters, estimation) on the estimates for the different parameters’ standard errors and the estimated level of bias (relative bias) in those estimates. The final results are reported as effect sizes (e.g., omega-squared or partial-omega-squared).

*Design Effects on Standard Error Estimates

*Design Effects on Relative Bias of Standard Errors

Confidence Interval Coverage

See notes on simulation for figure idea

*CI Coverage - Factor Loadings

Miscellanious Pages and other R scripts used

*Data Cleaning