Semi-automated Rasch analysis with differential item functioning.
DIF detection
Differential item functioning
GPCM-DIF
GPCMlasso
Generalized partial credit model
Penalized JMLE
Rasch model
Semi-automated Rasch analysis
Journal
Behavior research methods
ISSN: 1554-3528
Titre abrégé: Behav Res Methods
Pays: United States
ID NLM: 101244316
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
accepted:
30
07
2022
medline:
23
10
2023
pubmed:
8
9
2022
entrez:
7
9
2022
Statut:
ppublish
Résumé
Rasch analysis is a procedure to develop and validate instruments that aim to measure a person's traits. However, manual Rasch analysis is a complex and time-consuming task, even more so when the possibility of differential item functioning (DIF) is taken into consideration. Furthermore, manual Rasch analysis by construction relies on a modeler's subjective choices. As an alternative approach, we introduce a semi-automated procedure that is based on the optimization of a new criterion, called in-plus-out-of-questionnaire log likelihood with differential item functioning (IPOQ-LL-DIF), which extends our previous criterion. We illustrate our procedure on artificially generated data as well as on several real-world datasets containing potential DIF items. On these real-world datasets, our procedure found instruments with similar clinimetric properties as those suggested by experts through manual analyses.
Identifiants
pubmed: 36070131
doi: 10.3758/s13428-022-01947-9
pii: 10.3758/s13428-022-01947-9
pmc: PMC10556135
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3129-3148Informations de copyright
© 2022. The Author(s).
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