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Diagnostic Classification Model for Forced-Choice Items and Noncognitive Tests

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Title: Diagnostic Classification Model for Forced-Choice Items and Noncognitive Tests
Language: English
Authors: Huang, Hung-Yu (ORCID 0000-0001-6244-1950)
Source: Educational and Psychological Measurement. Feb 2023 83(1):146-180.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 35
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Test Items, Classification, Bayesian Statistics, Decision Making, Models, Guidelines, Response Style (Tests), Simulation, Comparative Analysis, Accuracy, Item Response Theory, Test Format, Psychological Patterns, Diagnostic Tests
DOI: 10.1177/00131644211069906
ISSN: 0013-1644
1552-3888
Abstract: The forced-choice (FC) item formats used for noncognitive tests typically develop a set of response options that measure different traits and instruct respondents to make judgments among these options in terms of their preference to control the response biases that are commonly observed in normative tests. Diagnostic classification models (DCMs) can provide information regarding the mastery status of test takers on latent discrete variables and are more commonly used for cognitive tests employed in educational settings than for noncognitive tests. The purpose of this study is to develop a new class of DCM for FC items under the higher-order DCM framework to meet the practical demands of simultaneously controlling for response biases and providing diagnostic classification information. By conducting a series of simulations and calibrating the model parameters with a Bayesian estimation, the study shows that, in general, the model parameters can be recovered satisfactorily with the use of long tests and large samples. More attributes improve the precision of the second-order latent trait estimation in a long test, but decrease the classification accuracy and the estimation quality of the structural parameters. When statements are allowed to load on two distinct attributes in paired comparison items, the specific-attribute condition produces better a parameter estimation than the overlap-attribute condition. Finally, an empirical analysis related to work-motivation measures is presented to demonstrate the applications and implications of the new model.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1360527
Database: ERIC
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  Data: <searchLink fieldCode="SO" term="%22Educational+and+Psychological+Measurement%22"><i>Educational and Psychological Measurement</i></searchLink>. Feb 2023 83(1):146-180.
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  Data: The forced-choice (FC) item formats used for noncognitive tests typically develop a set of response options that measure different traits and instruct respondents to make judgments among these options in terms of their preference to control the response biases that are commonly observed in normative tests. Diagnostic classification models (DCMs) can provide information regarding the mastery status of test takers on latent discrete variables and are more commonly used for cognitive tests employed in educational settings than for noncognitive tests. The purpose of this study is to develop a new class of DCM for FC items under the higher-order DCM framework to meet the practical demands of simultaneously controlling for response biases and providing diagnostic classification information. By conducting a series of simulations and calibrating the model parameters with a Bayesian estimation, the study shows that, in general, the model parameters can be recovered satisfactorily with the use of long tests and large samples. More attributes improve the precision of the second-order latent trait estimation in a long test, but decrease the classification accuracy and the estimation quality of the structural parameters. When statements are allowed to load on two distinct attributes in paired comparison items, the specific-attribute condition produces better a parameter estimation than the overlap-attribute condition. Finally, an empirical analysis related to work-motivation measures is presented to demonstrate the applications and implications of the new model.
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        Value: 10.1177/00131644211069906
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 35
        StartPage: 146
    Subjects:
      – SubjectFull: Test Items
        Type: general
      – SubjectFull: Classification
        Type: general
      – SubjectFull: Bayesian Statistics
        Type: general
      – SubjectFull: Decision Making
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      – SubjectFull: Models
        Type: general
      – SubjectFull: Guidelines
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      – SubjectFull: Response Style (Tests)
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      – SubjectFull: Simulation
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      – SubjectFull: Comparative Analysis
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      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Test Format
        Type: general
      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Diagnostic Tests
        Type: general
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      – TitleFull: Diagnostic Classification Model for Forced-Choice Items and Noncognitive Tests
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              Type: published
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