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Article: Mixture Item Response Models for Inattentive Responding Behavior

TitleMixture Item Response Models for Inattentive Responding Behavior
Authors
Issue Date2018
PublisherSage Publications, Inc. The Journal's web site is located at http://www.sagepub.com/journal.aspx?pid=146
Citation
Organizational Research Methods, 2018, v. 21 n. 1, p. 197-225 How to Cite?
AbstractInattentive responses can threaten measurement quality, yet they are common in rating- or Likert-scale data. In this study, we proposed a new mixture item response theory model to distinguish inattentive responses from normal responses so that test validity can be ascertained. Simulation studies demonstrated that the parameters of the new model were recovered fairly well using the Bayesian methods implemented in the freeware WinBUGS, and fitting the new model to data that lacked inattentive responses did not result in severely biased parameter estimates. In contrast, ignoring inattentive responses by fitting standard item response theory models to data containing inattentive responses yielded seriously biased parameter estimates and a failure to distinguish inattentive participants from normal participants; the person-fit statistic lz was also unsatisfactory in identifying inattentive responses. Two empirical examples demonstrate the applications of the new model.
Persistent Identifierhttp://hdl.handle.net/10722/258755
ISSN
2019 Impact Factor: 5.705
2015 SCImago Journal Rankings: 5.705
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJin, KY-
dc.contributor.authorChen, HF-
dc.contributor.authorWang, WC-
dc.date.accessioned2018-08-22T01:43:34Z-
dc.date.available2018-08-22T01:43:34Z-
dc.date.issued2018-
dc.identifier.citationOrganizational Research Methods, 2018, v. 21 n. 1, p. 197-225-
dc.identifier.issn1094-4281-
dc.identifier.urihttp://hdl.handle.net/10722/258755-
dc.description.abstractInattentive responses can threaten measurement quality, yet they are common in rating- or Likert-scale data. In this study, we proposed a new mixture item response theory model to distinguish inattentive responses from normal responses so that test validity can be ascertained. Simulation studies demonstrated that the parameters of the new model were recovered fairly well using the Bayesian methods implemented in the freeware WinBUGS, and fitting the new model to data that lacked inattentive responses did not result in severely biased parameter estimates. In contrast, ignoring inattentive responses by fitting standard item response theory models to data containing inattentive responses yielded seriously biased parameter estimates and a failure to distinguish inattentive participants from normal participants; the person-fit statistic lz was also unsatisfactory in identifying inattentive responses. Two empirical examples demonstrate the applications of the new model.-
dc.languageeng-
dc.publisherSage Publications, Inc. The Journal's web site is located at http://www.sagepub.com/journal.aspx?pid=146-
dc.relation.ispartofOrganizational Research Methods-
dc.rightsOrganizational Research Methods. Copyright © Sage Publications, Inc.-
dc.titleMixture Item Response Models for Inattentive Responding Behavior-
dc.typeArticle-
dc.identifier.emailJin, KY: kyjin@hku.hk-
dc.description.naturepostprint-
dc.identifier.doi10.1177/1094428117725792-
dc.identifier.scopuseid_2-s2.0-85038218161-
dc.identifier.hkuros286634-
dc.identifier.volume21-
dc.identifier.issue1-
dc.identifier.spage197-
dc.identifier.epage225-
dc.identifier.isiWOS:000418043300007-
dc.publisher.placeUnited States-

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