Iterative Item Selection of Neighborhood Clusters: A Nonparametric and Non-IRT Method for Generating Miniature Computer Adaptive Questionnaires.
The questionnaire method has always been an important research method in psychology. The increasing prevalence of multidimensional trait measures in psychological research has led researchers to use longer questionnaires. However, questionnaires that are too long will inevitably reduce the quality o...
| Publicado en: | Educational & Psychological Measurement Vol. 84; no. 2; pp. 364 - 387 |
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| Formato: | Artículo |
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Sage Publications Inc.
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=175872140&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 175872140 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Apr2024 vid: 84 iid: 2 pid: 344 pub: Sage Publications Inc. artinfo: ui: 175872140 10.1177/00131644231176053 ppf: 364 ppct: 23 formats: tig: atl: Iterative Item Selection of Neighborhood Clusters: A Nonparametric and Non-IRT Method for Generating Miniature Computer Adaptive Questionnaires. aug: au: Xu, Yongze affil: Beijing Normal University, Zhuhai, China su: Computer adaptive testing Statistical models Scale analysis (Psychology) Questionnaires Simulation methods in education Machine learning Algorithms sug: subj: Computer adaptive testing Statistical models Scale analysis (Psychology) Questionnaires Simulation methods in education Machine learning Algorithms keyword: computer adaptive test item selection Likert-type scale machine learning personality measures Questionnaire length computer adaptive test item selection Likert-type scale machine learning personality measures Questionnaire length ab: The questionnaire method has always been an important research method in psychology. The increasing prevalence of multidimensional trait measures in psychological research has led researchers to use longer questionnaires. However, questionnaires that are too long will inevitably reduce the quality of the completed questionnaires and the efficiency of collection. Computer adaptive testing (CAT) can be used to reduce the test length while preserving the measurement accuracy. However, it is more often used in aptitude testing and involves a large number of parametric assumptions. Applying CAT to psychological questionnaires often requires question-specific model design and preexperimentation. The present article proposes a nonparametric and item response theory (IRT)-independent CAT algorithm. The new algorithm is simple and highly generalizable. It can be quickly used in a variety of questionnaires and tests without being limited by theoretical assumptions in different research areas. Simulation and empirical studies were conducted to demonstrate the validity of the new algorithm in aptitude tests and personality measures. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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