A Bayesian region of measurement equivalence (ROME) approach for establishing measurement invariance

The current study introduces the Bayesian region of measurement equivalence (ROME) method for visualizing and quantifying such biases. ROME estimates the most probable magnitudes of test bias for individuals with different construct scores and compares it to a predefined region of tolerable bias levels.

Adjusting for partial invariance in latent parameter estimation: Comparing forward specification search and approximate invariance methods

A series of simulation studies which found that alignment optimization works well for adjusting for measurement noninvariance in few groups.

Composite reliability of multilevel data: It's about observed scores and construct meanings

This article shows that the previously proposed between-level composite reliability can provide overly optimistic reliability coefficient because it ignores one major source of error, namely the sampling error of cluster means. To obtain more accurate reliability information for multilevel data, this article proposes alternative reliability indices that correctly account for the different sources of measurement error.

Weighted Least Squares

Load packages Data Polychoric Correlations lavaan OpenMx Weighted Least Squares Estimation One-factor model Standard Errors Final thoughts Recently I was working on a revision for a paper that involves structural equation modeling with categorical observed variables, and it uses a robust variant of weighted least square (also called asymptotic distribution free) estimators.

Using Julia to Find MLE for a Factor Model

Set Seed Generate Univarate Normal Data Generate Multivariate Normal Data I’ve been staying home for a bit more than a week now. While keep working on my research, I also think it may help fill my time by picking up some skills.

Understanding the impact of partial factorial invariance on selection accuracy: An R script

We briefly review the selection accuracy analysis for partial invariance and provide a user-friendly R script (also available as a Web application) that takes parameter estimates as input, automatically produces summary statistics for evaluating selection accuracy, and generates a graph for visualizing the results. Hypothetical and real data examples are provided to illustrate the use of the R script.