scholarly journals Full Information Optimal Scoring

2019 ◽  
Vol 45 (3) ◽  
pp. 297-315 ◽  
Author(s):  
James Ramsay ◽  
Marie Wiberg ◽  
Juan Li
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Test Data ◽  
Test Performance ◽  
Mean Squared Error ◽  
Full Information ◽  
Response Theory ◽  
Root Mean Squared Error ◽  
Closed Intervals ◽  
Squared Error

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.

An item response theory model of matching test performance

2020 ◽  
Vol 95 ◽  
pp. 102327 ◽  
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

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

2017 ◽  
Vol 24 (5) ◽  
pp. 897-902 ◽  
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.

scholarly journals Stages of moral judgment development: Applying item response theory to Defining Issues Test data

2018 ◽  
Vol 48 (4) ◽  
pp. 423-438 ◽  
Author(s):  
Thijs van den Enden ◽  
Jan Boom ◽  
Daniel Brugman ◽  
Stephen Thoma
Keyword(s):  
Item Response Theory ◽  
Moral Judgment ◽  
Item Response ◽  
Test Data ◽  
Defining Issues Test ◽  
Response Theory

Evaluation of Attitude Survey Translations an Investigation Using Item Response Theory

1989 ◽  
Vol 24 (1) ◽  
pp. 665-684 ◽  
Author(s):  
Barbara B. Ellis ◽  
Beate Minsel ◽  
Peter Becker
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Response Theory ◽  
Attitude Survey

How Do Psychologists Learn Item Response Theory?

2001 ◽  
Vol 46 (6) ◽  
pp. 629-632
Author(s):  
Robert J. Mislevy
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Response Theory
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