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Optimal Scores as an Alternative to Sum Scores
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0001-5549-8262
2018 (English)In: Quantitative Psychology: The 82nd Annual Meetingof the Psychometric Society, Zurich,Switzerland, 2017 / [ed] Marie Wiberg, Steven Culpepper, Rianne Janssen, Jorge González, & Dylan Molenaar, Cham, Switzerland: Springer , 2018, p. 1-10Chapter in book (Refereed)
Abstract [en]

This paper discusses the use of optimal scores as an alternative to sum scores and expected sum scores when analyzing test data. Optimal scores are built on nonparametric methods and use the interaction between the test takers´ responses on each item and the impact of the corresponding items on the estimate of their performance. Both theoretical arguments for optimal score as well as arguments built upon simulation results are given. The paper claims that in order to achieve the same accuracy in terms of mean squared error and root mean squared error, an optimally scored test needs substantially fewer items than a sum scored test. The top-performing test takers and the bottom 5% test takers are by far the groups that benefit most from using optimal scores.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer , 2018. p. 1-10
Series
Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009, E-ISSN 2194-1017 ; 233
Keywords [en]
Optimal scoring, Item impact, Sum scores, Expected sum scores
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-147090DOI: 10.1007/978-3-319-77249-3ISBN: 978-3-319-77248-6 (print)ISBN: 978-3-319-77249-3 (electronic)OAI: oai:DiVA.org:umu-147090DiVA, id: diva2:1201760
Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2018-06-09

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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