Controlling Guessing Bias in the Dichotomous Rasch Model Applied to a Large-Scale, Vertically Scaled Testing Program
- PMID: 29795871
- PMCID: PMC5965560
- DOI: 10.1177/0013164415594202
Controlling Guessing Bias in the Dichotomous Rasch Model Applied to a Large-Scale, Vertically Scaled Testing Program
Abstract
Recent research has shown how the statistical bias in Rasch model difficulty estimates induced by guessing in multiple-choice items can be eliminated. Using vertical scaling of a high-profile national reading test, it is shown that the dominant effect of removing such bias is a nonlinear change in the unit of scale across the continuum. The consequence is that the proficiencies of the more proficient students are increased relative to those of the less proficient. Not controlling the guessing bias underestimates the progress of students across 7 years of schooling with important educational implications.
Keywords: Rasch model; guessing; large-scale assessments; multiple-choice items; vertical scaling.
Conflict of interest statement
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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References
-
- Andrich D. (2004). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, 42, 7-16. - PubMed
-
- Andrich D., Marais I., Humphry S. (2012). Using a theorem by Andersen and the dichotomous Rasch model to assess the presence of random guessing in multiple choice items. Journal of Educational and Behavioral Statistics, 37, 417-442.
-
- Andrich D., Marais I. (2014). Person proficiency estimates in the dichotomous Rasch model when random guessing is removed from difficulty estimates of multiple choice items. Applied Psychological Measurement, 36, 432-449.
-
- Andrich D., Sheridan B. S., Luo G. (2013). RUMM2030: An MS Windows computer program for the analysis of data according to Rasch Unidimensional Models for Measurement. Perth, Australia: RUMM Laboratory.
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