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Harmonic Regression and Scale Stability

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  • Yi-Hsuan Lee
  • Shelby Haberman

Abstract

Monitoring a very frequently administered educational test with a relatively short history of stable operation imposes a number of challenges. Test scores usually vary by season, and the frequency of administration of such educational tests is also seasonal. Although it is important to react to unreasonable changes in the distributions of test scores in a timely fashion, it is not a simple matter to ascertain what sort of distribution is really unusual. Many commonly used approaches for seasonal adjustment are designed for time series with evenly spaced observations that span many years and, therefore, are inappropriate for data from such educational tests. Harmonic regression, a seasonal-adjustment method, can be useful in monitoring scale stability when the number of years available is limited and when the observations are unevenly spaced. Additional forms of adjustments can be included to account for variability in test scores due to different sources of population variations. To illustrate, real data are considered from an international language assessment. Copyright The Psychometric Society 2013

Suggested Citation

  • Yi-Hsuan Lee & Shelby Haberman, 2013. "Harmonic Regression and Scale Stability," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 815-829, October.
    • Handle: RePEc:spr:psycho:v:78:y:2013:i:4:p:815-829
    DOI: 10.1007/s11336-013-9337-1
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    References listed on IDEAS

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    1. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    2. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    3. 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.
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    Cited by:

    1. Michela Battauz, 2017. "Multiple Equating of Separate IRT Calibrations," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 610-636, September.
    2. 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.
    3. 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.
    4. Björn Andersson & Alina Davier, 2015. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 856-858, September.
    5. 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.

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