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Monitoring Scale Scores over Time via Quality Control Charts, Model-Based Approaches, and Time Series Techniques

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  • Yi-Hsuan Lee
  • Alina Davier

Abstract

Maintaining a stable score scale over time is critical for all standardized educational assessments. Traditional quality control tools and approaches for assessing scale drift either require special equating designs, or may be too time-consuming to be considered on a regular basis with an operational test that has a short time window between an administration and its score reporting. Thus, the traditional methods are not sufficient to catch unusual testing outcomes in a timely manner. This paper presents a new approach for score monitoring and assessment of scale drift. It involves quality control charts, model-based approaches, and time series techniques to accommodate the following needs of monitoring scale scores: continuous monitoring, adjustment of customary variations, identification of abrupt shifts, and assessment of autocorrelation. Performance of the methodologies is evaluated using manipulated data based on real responses from 71 administrations of a large-scale high-stakes language assessment. Copyright The Psychometric Society 2013

Suggested Citation

  • Yi-Hsuan Lee & Alina Davier, 2013. "Monitoring Scale Scores over Time via Quality Control Charts, Model-Based Approaches, and Time Series Techniques," Psychometrika, Springer;The Psychometric Society, vol. 78(3), pages 557-575, July.
    • Handle: RePEc:spr:psycho:v:78:y:2013:i:3:p:557-575
    DOI: 10.1007/s11336-013-9317-5
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    References listed on IDEAS

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    1. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    2. Achim Zeileis, 2005. "A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 445-466.
    3. Visser, Ingmar & Speekenbrink, Maarten, 2010. "depmixS4: An R Package for Hidden Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i07).
    4. Yi-Hsuan Lee & Shelby Haberman, 2013. "Harmonic Regression and Scale Stability," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 815-829, October.
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    Cited by:

    1. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    2. Shelley H. Liu & Yitong Chen & Jordan R. Kuiper & Emily Ho & Jessie P. Buckley & Leah Feuerstahler, 2024. "Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 482-502, July.
    3. Yunxiao Chen & Yi-Hsuan Lee & Xiaoou Li, 2022. "Item Pool Quality Control in Educational Testing: Change Point Model, Compound Risk, and Sequential Detection," Journal of Educational and Behavioral Statistics, , vol. 47(3), pages 322-352, June.
    4. Chen, Yunxiao & Lee, Yi-Hsuan & Li, Xiaoou, 2022. "Item pool quality control in educational testing: change point model, compound risk, and sequential detection," LSE Research Online Documents on Economics 112498, London School of Economics and Political Science, LSE Library.
    5. Sandip Sinharay, 2016. "Person Fit Analysis in Computerized Adaptive Testing Using Tests for a Change Point," Journal of Educational and Behavioral Statistics, , vol. 41(5), pages 521-549, October.
    6. Yi-Hsuan Lee & Charles Lewis, 2021. "Monitoring Item Performance With CUSUM Statistics in Continuous Testing," Journal of Educational and Behavioral Statistics, , vol. 46(5), pages 611-648, October.
    7. Yi-Hsuan Lee & Shelby Haberman, 2013. "Harmonic Regression and Scale Stability," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 815-829, October.
    8. Sandip Sinharay, 2017. "Some Remarks on Applications of Tests for Detecting A Change Point to Psychometric Problems," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1149-1161, December.
    9. Michela Battauz, 2023. "Testing for differences in chain equating," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 134-145, May.
    10. Alina Davier, 2013. "Observed-Score Equating: An Overview," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 605-623, October.
    11. Björn Andersson & Alina Davier, 2015. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 856-858, September.

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