View in EDS

Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models

Saved in:
Bibliographic Details
Title: Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models
Language: English
Authors: Na Shan (ORCID 0000-0002-8590-3692), Ping-Feng Xu
Source: Journal of Educational and Behavioral Statistics. 2025 50(2):187-213.
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: 27
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Bayesian Statistics, Item Response Theory, Adolescents, Longitudinal Studies, Item Analysis, Psychological Studies, Behavioral Science Research, Models, Evaluation Methods, Comparative Analysis, Simulation, Sample Size, Delinquency, Rating Scales, Secondary School Students, Gender Differences
Assessment and Survey Identifiers: National Longitudinal Study of Adolescent Health
DOI: 10.3102/10769986241233777
ISSN: 1076-9986
1935-1054
Abstract: The detection of differential item functioning (DIF) is important in psychological and behavioral sciences. Standard DIF detection methods perform an item-by-item test iteratively, often assuming that all items except the one under investigation are DIF-free. This article proposes a Bayesian adaptive Lasso method to detect DIF in graded response models (GRMs), where the DIF effects for all items can be identified simultaneously. The multiple-group GRMs are specified, and the possible DIF effects for each item are reparameterized using the increment components. Then, a Bayesian adaptive Lasso procedure is developed for parameter estimation, in which DIF effects can be automatically obtained. Our method is evaluated and compared with the commonly used likelihood ratio test method in a simulation study. The results show that our method can recover most model parameters well and has better control of false positive rates in almost all conditions. An application is presented using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health).
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1468107
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1468107
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Na+Shan%22">Na Shan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8590-3692">0000-0002-8590-3692</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ping-Feng+Xu%22">Ping-Feng Xu</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+and+Behavioral+Statistics%22"><i>Journal of Educational and Behavioral Statistics</i></searchLink>. 2025 50(2):187-213.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: 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
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 27
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Bayesian+Statistics%22">Bayesian Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Analysis%22">Item Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Studies%22">Psychological Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+Science+Research%22">Behavioral Science Research</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Sample+Size%22">Sample Size</searchLink><br /><searchLink fieldCode="DE" term="%22Delinquency%22">Delinquency</searchLink><br /><searchLink fieldCode="DE" term="%22Rating+Scales%22">Rating Scales</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22National+Longitudinal+Study+of+Adolescent+Health%22">National Longitudinal Study of Adolescent Health</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3102/10769986241233777
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1076-9986<br />1935-1054
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The detection of differential item functioning (DIF) is important in psychological and behavioral sciences. Standard DIF detection methods perform an item-by-item test iteratively, often assuming that all items except the one under investigation are DIF-free. This article proposes a Bayesian adaptive Lasso method to detect DIF in graded response models (GRMs), where the DIF effects for all items can be identified simultaneously. The multiple-group GRMs are specified, and the possible DIF effects for each item are reparameterized using the increment components. Then, a Bayesian adaptive Lasso procedure is developed for parameter estimation, in which DIF effects can be automatically obtained. Our method is evaluated and compared with the commonly used likelihood ratio test method in a simulation study. The results show that our method can recover most model parameters well and has better control of false positive rates in almost all conditions. An application is presented using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health).
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1468107
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1468107
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3102/10769986241233777
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 187
    Subjects:
      – SubjectFull: Bayesian Statistics
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Adolescents
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Item Analysis
        Type: general
      – SubjectFull: Psychological Studies
        Type: general
      – SubjectFull: Behavioral Science Research
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Comparative Analysis
        Type: general
      – SubjectFull: Simulation
        Type: general
      – SubjectFull: Sample Size
        Type: general
      – SubjectFull: Delinquency
        Type: general
      – SubjectFull: Rating Scales
        Type: general
      – SubjectFull: Secondary School Students
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: National Longitudinal Study of Adolescent Health
        Type: general
    Titles:
      – TitleFull: Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Na Shan
      – PersonEntity:
          Name:
            NameFull: Ping-Feng Xu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1076-9986
            – Type: issn-electronic
              Value: 1935-1054
          Numbering:
            – Type: volume
              Value: 50
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Educational and Behavioral Statistics
              Type: main
ResultId 1