A New Perspective on Measurement Invariance

In this project, we develop new data-driven methods to a) investigate measurement invariance more holistically (i.e., with regard to many covariates and without constraining the analysis model too much), b) address measurement invariance at the early stages of scale development, c) inform a causal perspective on measurement, and d) ultimately make psychological measurements more robust and generalizable.

Figure 1: Visualization of the new EGA trees approach that can be used to identify differences in the latent dimensionality as the cause of configural non-invariance.

Selected Publications and Preprints:

Sterner, P., & Goretzko, D. (2024) Exploratory Factor Analysis Trees: Evaluating Measurement Invariance Between Multiple Covariates. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2023.2188573

Sterner, P., Pargent, F., Deffner, D., & Goretzko, D. (2024) A Causal Framework for the Comparability of Latent Variables. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2024.2339396

Sterner, P., de Roover, K., & Goretzko, D. (2025) New Developments in Measurement Invariance Testing-An Overview and Comparison of EFA-based Approaches. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2024.2393647

Straub, N., Goretzko, D., & Sterner, P. (2025) Assessing Measurement Invariance with Exploratory Factor Analysis Trees: A Practical Guide. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000898

Goretzko, D., & Sterner, P. (2025). Exploratory Graph Analysis Trees-A Network-based Approach to Investigate Measurement Invariance with Numerous Covariates. Psychological Methods. https://doi.org/10.1037/met0000796

Schuhbeck, T. M. B., Sterner, P., & Goretzko, D. (2025). Quantifying Measurement Non-Invariance Beyond Simple Structure: The Closed Formulas of Universal Effect Size Measures for MI. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2025.2570447

Sterner, P., Pargent, F., & Goretzko, D. (2026). Don’t let MI be misunderstood: Measurement invariance is more than a statistical assumption. Current Research in Ecological and Social Psychology. https://doi.org/10.1016/j.cresp.2025.100261

Goretzko, D., Howard, M. C., & Sterner, P. (2026). Investigating Measurement Invariance for Multiple Covariates in Organizational Research using EFA and CFA Trees. Journal of Applied Psychology. https://doi.org/10.1037/apl0001368

Sterner, P., & Goretzko, D. (under review) The mismeasure of comparability: Non-invariance and nomological validity in cross-cultural psychology—A commentary on Kusano et al.(2025). PsyArXiv Preprint. https://doi.org/10.31234/osf.io/pnu8w_v2