A Family of Discrete Kernels for Presmoothing Test Score Distributions
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Date
2024
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Publisher
Springer
Abstract
In the fields of educational measurement and testing, score distributions are often estimated by the sample relative frequency distribution. As many score distributions are discrete and may have irregularities, it has been common practice to use presmoothing techniques to correct for such irregularities of the score distributions. A common way to conduct presmoothing has been to use log-linear models. In this chapter, we introduce a novel class of discrete kernels that can effectively estimate the probability mass function of scores, providing a presmoothing solution. The chapter includes an empirical illustration demonstrating that the proposed discrete kernel estimates perform as well as or better than the existing methods like log-linear models in presmoothing score distributions. The practical implications of this finding are discussed, highlighting the potential benefits of using discrete kernels in educational measurement contexts. Additionally, the chapter identifies several areas for further research, indicating opportunities for advancing the field’s methodology and practices.
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Keywords
Discrete kernels, Irregularities, Presmoothing, Score distributions