measurement invariance

Correcting for unreliability and partial invariance: A two-stage path analysis approach

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.

What to Do If Measurement Invariance Does Not Hold? Let's Look at the Practical Significance

Measurement invariance---that a test measures the same construct in the same way across subgroups---needs to hold for subgroup comparisons to be meaningful. There has been tremendous growth in measurement invariance research in the past decade. …

Classification accuracy of multidimensional tests: Quantifying the impact of noninvariance

We extend Millsap & Kwok's framework for examining the impact of noninvariance to a multidimensional test on classification.

Adjusting for Measurement Noninvariance With Alignment in Growth Modeling

The proposed AwC-growth method is a computationally efficient method to adjust for measurement noninvariance to obtain valid growth parameter estimates.

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.


A web app for performing selection accuracy analysis in the presence of partial invariance.

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.