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dc.date.accessioned2019-12-08T19:16:11Z
dc.date.available2019-12-08T19:16:11Z
dc.date.created2018-08-01T11:11:32Z
dc.date.issued2018
dc.identifier.citationPaap, Muirne C. S. Kroeze, Karel A. Glas, Cees A.W. Terwee, Caroline B. van der Palen, Job Veldkamp, Bernard P . Measuring Patient-Reported Outcomes Adaptively: Multidimensionality Matters!. Applied psychological measurement. 2018, 42(5), 327-342
dc.identifier.urihttp://hdl.handle.net/10852/71394
dc.description.abstractAs there is currently a marked increase in the use of both unidimensional (UCAT) and multidimensional computerized adaptive testing (MCAT) in psychological and health measurement, the main aim of the present study is to assess the incremental value of using MCAT rather than separate UCATs for each dimension. Simulations are based on empirical data that could be considered typical for health measurement: a large number of dimensions (4), strong correlations among dimensions (.77-.87), and polytomously scored response data. Both variable- (SE < .316, SE < .387) and fixed-length conditions (total test length of 12, 20, or 32 items) are studied. The item parameters and variance–covariance matrix Φ are estimated with the multidimensional graded response model (GRM). Outcome variables include computerized adaptive test (CAT) length, root mean square error (RMSE), and bias. Both simulated and empirical latent trait distributions are used to sample vectors of true scores. MCATs were generally more efficient (in terms of test length) and more accurate (in terms of RMSE) than their UCAT counterparts. Absolute average bias was highest for variable-length UCATs with termination rule SE < .387. Test length of variable-length MCATs was on average 20% to 25% shorter than test length across separate UCATs. This study showed that there are clear advantages of using MCAT rather than UCAT in a setting typical for health measurement.
dc.languageEN
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleMeasuring Patient-Reported Outcomes Adaptively: Multidimensionality Matters!
dc.typeJournal article
dc.creator.authorPaap, Muirne C. S.
dc.creator.authorKroeze, Karel A.
dc.creator.authorGlas, Cees A.W.
dc.creator.authorTerwee, Caroline B.
dc.creator.authorvan der Palen, Job
dc.creator.authorVeldkamp, Bernard P
cristin.unitcode185,18,7,0
cristin.unitnameCentre for Educational Measurement
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin1599307
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Applied psychological measurement&rft.volume=42&rft.spage=327&rft.date=2018
dc.identifier.jtitleApplied psychological measurement
dc.identifier.volume42
dc.identifier.issue5
dc.identifier.startpage327
dc.identifier.endpage342
dc.identifier.doihttps://doi.org/10.1177/0146621617733954
dc.identifier.urnURN:NBN:no-74531
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn0146-6216
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/71394/2/0146621617733954.pdf
dc.type.versionPublishedVersion


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