View in EDS HTML Full Text PDF Full Text

Semi‐automated Rasch analysis using in‐plus‐out‐of‐questionnaire log likelihood.

Saved in:
Bibliographic Details
Title: Semi‐automated Rasch analysis using in‐plus‐out‐of‐questionnaire log likelihood.
Authors: Wijayanto, Feri (AUTHOR), Mul, Karlien (AUTHOR), Groot, Perry (AUTHOR), van Engelen, Baziel G.M. (AUTHOR), Heskes, Tom (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. May2021, Vol. 74 Issue 2, p313-339. 27p.
Subjects: Procedure manuals, Integrated software, Rasch models
Abstract: Rasch analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch analysis is still considered to be a complex, labour‐intensive task, requiring human expertise and rather subjective judgements along the way. In this paper we propose a semi‐automated method for Rasch analysis based on first principles that reduces the need for human input. To this end, we introduce a novel criterion, called in‐plus‐out‐of‐questionnaire log likelihood (IPOQ‐LL). On artificial data sets, we confirm that optimization of IPOQ‐LL leads to the desired behaviour in the case of multi‐dimensional and inhomogeneous surveys. On three publicly available real‐world data sets, our method leads to instruments that are, for all practical purposes, indistinguishable from those obtained by Rasch analysis experts through a manual procedure. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 149782135
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Semi‐automated Rasch analysis using in‐plus‐out‐of‐questionnaire log likelihood.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wijayanto%2C+Feri%22">Wijayanto, Feri</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mul%2C+Karlien%22">Mul, Karlien</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Groot%2C+Perry%22">Groot, Perry</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22van+Engelen%2C+Baziel+G%2EM%2E%22">van Engelen, Baziel G.M.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Heskes%2C+Tom%22">Heskes, Tom</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. May2021, Vol. 74 Issue 2, p313-339. 27p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Procedure+manuals%22">Procedure manuals</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+software%22">Integrated software</searchLink><br /><searchLink fieldCode="DE" term="%22Rasch+models%22">Rasch models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Rasch analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch analysis is still considered to be a complex, labour‐intensive task, requiring human expertise and rather subjective judgements along the way. In this paper we propose a semi‐automated method for Rasch analysis based on first principles that reduces the need for human input. To this end, we introduce a novel criterion, called in‐plus‐out‐of‐questionnaire log likelihood (IPOQ‐LL). On artificial data sets, we confirm that optimization of IPOQ‐LL leads to the desired behaviour in the case of multi‐dimensional and inhomogeneous surveys. On three publicly available real‐world data sets, our method leads to instruments that are, for all practical purposes, indistinguishable from those obtained by Rasch analysis experts through a manual procedure. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=149782135
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bmsp.12218
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 313
    Subjects:
      – SubjectFull: Procedure manuals
        Type: general
      – SubjectFull: Integrated software
        Type: general
      – SubjectFull: Rasch models
        Type: general
    Titles:
      – TitleFull: Semi‐automated Rasch analysis using in‐plus‐out‐of‐questionnaire log likelihood.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wijayanto, Feri
      – PersonEntity:
          Name:
            NameFull: Mul, Karlien
      – PersonEntity:
          Name:
            NameFull: Groot, Perry
      – PersonEntity:
          Name:
            NameFull: van Engelen, Baziel G.M.
      – PersonEntity:
          Name:
            NameFull: Heskes, Tom
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2021
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 00071102
          Numbering:
            – Type: volume
              Value: 74
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: British Journal of Mathematical & Statistical Psychology
              Type: main
ResultId 1