Sequential generalized kernel equating: Providing comparable scores across multiple test forms with nonequivalent groups and differently measured covariates
Abstract
Test equating using covariates may be applied to provide comparable scores from multiple test forms when no anchor items are available. However, its performance may be compromised if some of the covariates themselves are measured using different test forms. In this work, we propose sequential generalized kernel equating to account for possible differences in the distribution of covariates used in the NEC design. We evaluate the proposed approach through a simulation study within the kernel equating framework. Results indicate that equating the covariate reduces bias in equated test scores, particularly when the covariate distributions differ and the correlation between the covariate and the test score is strong. A real data example from a national high school leaving examination further demonstrates the practical application.
Versions
Related
- When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments
- Generalized survival energy models with exogenous covariates for mortality prediction
- Doubly robust target inference for generalized linear regression with completely missing covariates
- Extrapolation of extreme covariates in generalized additive regression using extreme value theory
- Generalized Odds for Ordinal Outcomes: A Causal Inference Framework Adjusting for Covariates
BibTeX
@misc{varejkova2026sequential,
title = {{Sequential generalized kernel equating: Providing comparable scores across multiple test forms with nonequivalent groups and differently measured covariates}},
author = {Michaela Vařejková and Patrícia Martinková and Eva Potužníková},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.28559},
eprint = {2605.28559},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.28559},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Sequential generalized kernel equating: Providing comparable scores across multiple test forms with nonequivalent groups and differently measured covariates
AU - Michaela Vařejková
AU - Patrícia Martinková
AU - Eva Potužníková
PY - 2026
DA - 2026/10/17
PB - arXiv
DO - 10.48550/arxiv.2605.28559
UR - https://arxiv.org/abs/2605.28559
AB - Test equating using covariates may be applied to provide comparable scores from multiple test forms when no anchor items are available. However, its performance may be compromised if some of the covariates themselves are measured using different test forms. In this work, we propose sequential generalized kernel equating to account for possible differences in the distribution of covariates used in the NEC design. We evaluate the proposed approach through a simulation study within the kernel equating framework. Results indicate that equating the covariate reduces bias in equated test scores, particularly when the covariate distributions differ and the correlation between the covariate and the test score is strong. A real data example from a national high school leaving examination further demonstrates the practical application.
ER -