Work

Sequential Generalized Kernel Equating: Providing Comparable Scores Across Multiple Test Forms With Nonequivalent Groups and Differently Measured Covariates

Year: 2026

Type: article

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 nonequivalent groups with covariates (NEC) design. We evaluate the proposed approach through a simulation study within the generalized 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, and the discussion considers its potential use in linking and harmonizing scores from related psychological and behavioral measures.

Source: Journal of Educational and Behavioral Statistics

DOI: 10.3102/10769986261485375

Authors: Michaela Vařejková, Patrícia Martinková, Eva Potužníková

Institutions: Charles University, Czech Academy of Sciences, Institute of Computer Science

Language: English

Topic: Psychometric Methodologies and Testing

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $2,500