Evidence map›Paper›PMID 36070131›Full record

ArticleBehavior research methods2023

Semi-automated Rasch analysis with differential item functioning.

Feri Wijayanto, Ioan Gabriel Bucur, Karlien Mul, Perry Groot, Baziel G M van Engelen, Tom Heskes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. autoRasch: An R Package to Do Semi-Automated Rasch Analysis.Applied psychological measurement · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Feri WijayantoInstitute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands. f.wijayanto@cs.ru.nl.
Ioan Gabriel BucurInstitute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.
Karlien MulDepartment of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands.
Perry GrootInstitute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.
Baziel G M van EngelenDepartment of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands.
Tom HeskesInstitute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

PsychometricsHumansProbabilityReproducibility of ResultsSurveys and QuestionnairesDIF detectionDifferential item functioningGeneralized partial credit modelGPCM-DIFGPCMlassoPenalized JMLERasch modelSemi-automated Rasch analysis

Identifiers

PMID36070131
PMCPMC10556135

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.