Propensity Score Methods for Local Test Score Equating: Stratification and Inverse Probability Weighting
Gabriel Wallin, Marie Wiberg

TL;DR
This paper introduces two new propensity score-based methods, stratification and inverse probability weighting, for local test score equating when anchor tests are unavailable, improving fairness in score comparisons.
Contribution
The paper develops and evaluates novel propensity score methods for local test equating without anchor tests, extending existing methodologies to covariate-only scenarios.
Findings
Both methods effectively adjust for group differences.
Performance depends on covariate-ability correlation strength.
Empirical and simulation results support their utility.
Abstract
In test equating, ensuring score comparability across different test forms is crucial but particularly challenging when test groups are non-equivalent and no anchor test is available. Local test equating aims to satisfy Lord's equity requirement by conditioning equating transformations on individual-level information, typically using anchor test scores as proxies for latent ability. However, anchor tests are not always available in practice. This paper introduces two novel propensity score-based methods for local equating: stratification and inverse probability weighting (IPW). These methods use covariates to account for group differences, with propensity scores serving as proxies for latent ability differences between test groups. The stratification method partitions examinees into comparable groups based on similar propensity scores, while IPW assigns weights inversely proportional to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
