Developments in Psychometric Population Models for Technology-Based Large-Scale Assessments: An Overview of Challenges and Opportunities

Matthias von Davier, Lale Khorramdel, Qiwei He, Hyo Jeong Shin and Haiwen Chen
Additional contact information
Lale Khorramdel: National Board of Medical Examiners
Haiwen Chen: Educational Testing Service

Journal of Educational and Behavioral Statistics, 2019, vol. 44, issue 6, 671-705

Abstract: International large-scale assessments (ILSAs) transitioned from paper-based assessments to computer-based assessments (CBAs) facilitating the use of new item types and more effective data collection tools. This allows implementation of more complex test designs and to collect process and response time (RT) data. These new data types can be used to improve data quality and the accuracy of test scores obtained through latent regression (population) models. However, the move to a CBA also poses challenges for comparability and trend measurement, one of the major goals in ISLAs. We provide an overview of current methods used in ILSAs to examine and assure the comparability of data across different assessment modes and methods that improve the accuracy of test scores by making use of new data types provided by a CBA.

Keywords: latent regression; process data; mode effects; population model; timing data (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:44:y:2019:i:6:p:671-705

DOI: 10.3102/1076998619881789

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