View in EDS

Bayesian Diagnostics for Test Design and Analysis

Saved in:
Bibliographic Details
Title: Bayesian Diagnostics for Test Design and Analysis
Language: English
Authors: Silva, R. M., Guan, Y., Swartz, T. B.
Source: Journal on Efficiency and Responsibility in Education and Science. 2017 10(2):44-50.
Availability: Czech University of Life Sciences Prague. Czech University of Life Sciences Prague, Kamýcká 129, Prague 6 - Suchdol 165 00, Czech Republic. e-mail: editor@eriesjournal.com; Web site: https://www.eriesjournal.com/index.php/eries
Peer Reviewed: Y
Page Count: 7
Publication Date: 2017
Document Type: Journal Articles
Reports - Research
Descriptors: Item Response Theory, Bayesian Statistics, Test Construction, Markov Processes, Monte Carlo Methods, Statistical Inference, Statistical Distributions, Questionnaires, Programming Languages
ISSN: 2336-2375
Abstract: This paper attempts to bridge the gap between classical test theory and item response theory. It is demonstrated that the familiar and popular statistics used in classical test theory can be translated into a Bayesian framework where all of the advantages of the Bayesian paradigm can be realized. In particular, prior opinion can be introduced and inferences can be obtained using posterior distributions. In classical test theory, inferential decisions are based on the values of statistics that are calculated from the responses of subjects over various test questions. In the proposed approach, analogous "statistics" are constructed from the output of simulation from the posterior distribution. This leads to population-based inferences which focus on the properties of the test rather than the performance of specific subjects. The use of the JAGS [Just # Another Gibbs Sampler] programming language facilitates extensions to more complex scenarios involving the assessment of tests and questionnaires.
Abstractor: As Provided
Entry Date: 2019
Accession Number: EJ1213899
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1213899
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: EJ1213899
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Bayesian Diagnostics for Test Design and Analysis
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Silva%2C+R%2E+M%2E%22">Silva, R. M.</searchLink><br /><searchLink fieldCode="AR" term="%22Guan%2C+Y%2E%22">Guan, Y.</searchLink><br /><searchLink fieldCode="AR" term="%22Swartz%2C+T%2E+B%2E%22">Swartz, T. B.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+on+Efficiency+and+Responsibility+in+Education+and+Science%22"><i>Journal on Efficiency and Responsibility in Education and Science</i></searchLink>. 2017 10(2):44-50.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Czech University of Life Sciences Prague. Czech University of Life Sciences Prague, Kamýcká 129, Prague 6 - Suchdol 165 00, Czech Republic. e-mail: editor@eriesjournal.com; Web site: https://www.eriesjournal.com/index.php/eries
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 7
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2017
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+Statistics%22">Bayesian Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Construction%22">Test Construction</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+Processes%22">Markov Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Inference%22">Statistical Inference</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Distributions%22">Statistical Distributions</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+Languages%22">Programming Languages</searchLink>
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2336-2375
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper attempts to bridge the gap between classical test theory and item response theory. It is demonstrated that the familiar and popular statistics used in classical test theory can be translated into a Bayesian framework where all of the advantages of the Bayesian paradigm can be realized. In particular, prior opinion can be introduced and inferences can be obtained using posterior distributions. In classical test theory, inferential decisions are based on the values of statistics that are calculated from the responses of subjects over various test questions. In the proposed approach, analogous "statistics" are constructed from the output of simulation from the posterior distribution. This leads to population-based inferences which focus on the properties of the test rather than the performance of specific subjects. The use of the JAGS [Just # Another Gibbs Sampler] programming language facilitates extensions to more complex scenarios involving the assessment of tests and questionnaires.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2019
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1213899
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1213899
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
        StartPage: 44
    Subjects:
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Bayesian Statistics
        Type: general
      – SubjectFull: Test Construction
        Type: general
      – SubjectFull: Markov Processes
        Type: general
      – SubjectFull: Monte Carlo Methods
        Type: general
      – SubjectFull: Statistical Inference
        Type: general
      – SubjectFull: Statistical Distributions
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Programming Languages
        Type: general
    Titles:
      – TitleFull: Bayesian Diagnostics for Test Design and Analysis
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Silva, R. M.
      – PersonEntity:
          Name:
            NameFull: Guan, Y.
      – PersonEntity:
          Name:
            NameFull: Swartz, T. B.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2017
          Identifiers:
            – Type: issn-print
              Value: 2336-2375
          Numbering:
            – Type: volume
              Value: 10
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal on Efficiency and Responsibility in Education and Science
              Type: main
ResultId 1