A Latent Class Signal Detection Model for Rater Scoring with Ordered Perceptual Distributions

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Bibliographic Details
Title: A Latent Class Signal Detection Model for Rater Scoring with Ordered Perceptual Distributions
Language: English
Authors: DeCarlo, Lawrence T., Zhou, Xiaoliang
Source: Journal of Educational Measurement. Spr 2021 58(1):31-53.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 23
Publication Date: 2021
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Scoring, Models, Bias, Perception, Accuracy, Language Tests
DOI: 10.1111/jedm.12265
ISSN: 0022-0655
Abstract: In signal detection rater models for constructed response (CR) scoring, it is assumed that raters discriminate equally well between different latent classes defined by the scoring rubric. An extended model that relaxes this assumption is introduced; the model recognizes that a rater may not discriminate equally well between some of the scoring classes. The extension recognizes a different type of rater effect and is shown to offer useful tests and diagnostic plots of the equal discrimination assumption, along with ways to assess rater accuracy and various rater effects. The approach is illustrated with an application to a large-scale language test.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1290434
Database: ERIC
Description
Abstract:In signal detection rater models for constructed response (CR) scoring, it is assumed that raters discriminate equally well between different latent classes defined by the scoring rubric. An extended model that relaxes this assumption is introduced; the model recognizes that a rater may not discriminate equally well between some of the scoring classes. The extension recognizes a different type of rater effect and is shown to offer useful tests and diagnostic plots of the equal discrimination assumption, along with ways to assess rater accuracy and various rater effects. The approach is illustrated with an application to a large-scale language test.
ISSN:0022-0655
DOI:10.1111/jedm.12265