Full Information Optimal Scoring

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.

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A Comparison of the Rasch Model and Constrained Item Response Theory Models for Pertinent Psychological Test Data

Multivariate and Mixture Distribution Rasch Models ◽

10.1007/978-0-387-49839-3_19 ◽

2007 ◽

pp. 293-309 ◽

Cited By ~ 1

Author(s):

Klaus D. Kubinger ◽

Clemens Draxler

Keyword(s):

Item Response Theory ◽

Item Response ◽

Test Data ◽

Rasch Model ◽

Psychological Test ◽

Response Theory ◽

The Rasch Model ◽

Item Response Theory Models

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Practical Consequences of Item Response Theory Model Misfit in the Context of Test Equating with Mixed-Format Test Data

Frontiers in Psychology ◽

10.3389/fpsyg.2017.00484 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 5

Author(s):

Yue Zhao ◽

Ronald K. Hambleton

Keyword(s):

Item Response Theory ◽

Item Response ◽

Test Data ◽

Theory Model ◽

Item Response Theory Model ◽

Test Equating ◽

Response Theory ◽

Model Misfit ◽

Mixed Format

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Applications of Multidimensional Item Response Theory Models With Covariates to Longitudinal Test Data

ETS Research Report Series ◽

10.1002/ets2.12108 ◽

2016 ◽

Vol 2016 (1) ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Jianbin Fu

Keyword(s):

Item Response Theory ◽

Item Response ◽

Test Data ◽

Multidimensional Item Response Theory ◽

Multidimensional Item Response ◽

Response Theory ◽

Item Response Theory Models

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An item response theory model of matching test performance

Journal of Mathematical Psychology ◽

10.1016/j.jmp.2020.102327 ◽

2020 ◽

Vol 95 ◽

pp. 102327 ◽

Cited By ~ 1

Author(s):

Matthew D. Zeigenfuse ◽

William H. Batchelder ◽

Mark Steyvers

Keyword(s):

Item Response Theory ◽

Item Response ◽

Test Performance ◽

Theory Model ◽

Item Response Theory Model ◽

Response Theory ◽

Matching Test

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Advancing the efficiency and efficacy of patient reported outcomes with multivariate computer adaptive testing

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx003 ◽

2017 ◽

Vol 24 (5) ◽

pp. 897-902 ◽

Cited By ~ 5

Author(s):

Scott Morris ◽

Mike Bass ◽

Mirinae Lee ◽

Richard E Neapolitan

Keyword(s):

Item Response Theory ◽

Item Response ◽

Patient Reported Outcomes ◽

Mean Squared Error ◽

Adaptive Testing ◽

Computer Adaptive Testing ◽

Measurement Information ◽

Response Theory ◽

Patient Reported ◽

Patient Burden

Abstract Objective: The Patient Reported Outcomes Measurement Information System (PROMIS) initiative developed an array of patient reported outcome (PRO) measures. To reduce the number of questions administered, PROMIS utilizes unidimensional item response theory and unidimensional computer adaptive testing (UCAT), which means a separate set of questions is administered for each measured trait. Multidimensional item response theory (MIRT) and multidimensional computer adaptive testing (MCAT) simultaneously assess correlated traits. The objective was to investigate the extent to which MCAT reduces patient burden relative to UCAT in the case of PROs. Methods: One MIRT and 3 unidimensional item response theory models were developed using the related traits anxiety, depression, and anger. Using these models, MCAT and UCAT performance was compared with simulated individuals. Results: Surprisingly, the root mean squared error for both methods increased with the number of items. These results were driven by large errors for individuals with low trait levels. A second analysis focused on individuals aligned with item content. For these individuals, both MCAT and UCAT accuracies improved with additional items. Furthermore, MCAT reduced the test length by 50%. Discussion: For the PROMIS Emotional Distress banks, neither UCAT nor MCAT provided accurate estimates for individuals at low trait levels. Because the items in these banks were designed to detect clinical levels of distress, there is little information for individuals with low trait values. However, trait estimates for individuals targeted by the banks were accurate and MCAT asked substantially fewer questions. Conclusion: By reducing the number of items administered, MCAT can allow clinicians and researchers to assess a wider range of PROs with less patient burden.

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