An analysis on the optimal number of options in multiple-choice items of the National Assessment of Educational Achievement

2014 ◽  
Vol 21 (2) ◽  
pp. 107-128
Author(s):  
Young-Ju Lee ◽  
Keyword(s):  
Educational Achievement ◽  
Multiple Choice ◽  
Optimal Number ◽  
National Assessment ◽  
Multiple Choice Items

Use of an Inclusive Option and the Optimal Number of Options for Multiple-Choice Items

1993 ◽  
Vol 53 (1) ◽  
pp. 241-247 ◽  
Author(s):  
Kevin D. Crehan ◽  
Thomas M. Haladyna ◽  
Britton W. Brewer
Keyword(s):  
Multiple Choice ◽  
Optimal Number ◽  
Multiple Choice Items

Automated Scoring of Paper-and-Pencil Figural Responses

1992 ◽  
Vol 20 (4) ◽  
pp. 251-260 ◽  
Author(s):  
Michael E. Martinez ◽  
John J. Ferris ◽  
William Kraft ◽  
Winton H. Manning
Keyword(s):  
Large Scale ◽  
Item Type ◽  
Multiple Choice ◽  
Automated Scoring ◽  
Educational Progress ◽  
National Assessment ◽  
Large Scale Testing ◽  
Computer Based ◽  
Multiple Choice Items ◽  
Scoring Algorithms

Large-scale testing is dominated by the multiple-choice question format. Widespread use of the format is explained, in part, by the ease with which multiple-choice items can be scored automatically. This article examines automated scoring procedures for an alternative item type: figural response. Figural response items call for the completion or modification of figural material, including illustrations, diagrams, and graphs. Nineteen science items were written in cooperation with the National Assessment of Educational Progress and printed with a special ink, invisible to scanning equipment. The items were answered with pencils; response sheets were then scanned and the resulting data were processed by computer-based scoring algorithms. Implications of this technology for the future of large-scale testing are discussed.

Certainty-Based Marking on Multiple-Choice Items: Psychometrics Meets Decision Theory

Psychometrika ◽  
2021 ◽  
Author(s):  
Qian Wu ◽  
Monique Vanerum ◽  
Anouk Agten ◽  
Andrés Christiansen ◽  
Frank Vandenabeele ◽  
...  
Keyword(s):  
Decision Theory ◽  
Multiple Choice ◽  
Multiple Choice Items

What's Wrong with Three-Option Multiple Choice Items?

1987 ◽  
Vol 47 (2) ◽  
pp. 513-522 ◽  
Author(s):  
Steven V. Owen ◽  
Robin D. Froman
Keyword(s):  
Multiple Choice ◽  
Multiple Choice Items

scholarly journals FREE-RESPONSE AND MULTIPLE-CHOICE ITEMS: MEASURES OF THE SAME ABILITY?

1990 ◽  
Vol 1990 (1) ◽  
pp. i-29 ◽  
Author(s):  
Randy Elliot Bennett ◽  
Donald A. Rock ◽  
Minhwei Wang
Keyword(s):  
Multiple Choice ◽  
Free Response ◽  
Multiple Choice Items

Detecting Rater Biases in Sparse Rater-Mediated Assessment Networks

2021 ◽  
pp. 001316442098810
Author(s):  
Stefanie A. Wind ◽  
Yuan Ge
Keyword(s):  
Multiple Choice ◽  
Complete Data ◽  
Performance Assessments ◽  
Model Estimation ◽  
Different Types ◽  
Multiple Choice Items ◽  
Practical Constraints

Practical constraints in rater-mediated assessments limit the availability of complete data. Instead, most scoring procedures include one or two ratings for each performance, with overlapping performances across raters or linking sets of multiple-choice items to facilitate model estimation. These incomplete scoring designs present challenges for detecting rater biases, or differential rater functioning (DRF). The purpose of this study is to illustrate and explore the sensitivity of DRF indices in realistic sparse rating designs that have been documented in the literature that include different types and levels of connectivity among raters and students. The results indicated that it is possible to detect DRF in sparse rating designs, but the sensitivity of DRF indices varies across designs. We consider the implications of our findings for practice related to monitoring raters in performance assessments.

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