Full Information Optimal Scoring

James Ramsay, Marie Wiberg and Juan Li
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James Ramsay: McGill University
Marie Wiberg: Umeå University
Juan Li: Ottawa Hospital Research Institute

Journal of Educational and Behavioral Statistics, 2020, vol. 45, issue 3, 297-315

Abstract: Ramsay and Wiberg used a new version of item response theory that represents test performance over nonnegative closed intervals such as [0, 100] or [0, n ] and demonstrated that optimal scoring of binary test data yielded substantial improvements in point-wise root-mean-squared error and bias over number right or sum scoring. We extend these results by showing that optimal scoring of the full information in option choices produces about as much further improvement in these measures of score performance as was achieved by going from sum scoring to optimal binary scoring.

Keywords: item characteristic curves; multinomial log-odds transformation; log-odds slope weighting; sum scoring; optimal scoring; two-stage test data analysis (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:45:y:2020:i:3:p:297-315

DOI: 10.3102/1076998619885636

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