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Conference Paper: Adjusting person fit index for skewness in cognitive diagnosis modeling

TitleAdjusting person fit index for skewness in cognitive diagnosis modeling
Authors
Santos, Kde la Torre, Jvon Davier, M
Issue Date2017
Citation
The Annual Meeting of National Council on Measurement in Education (NCME): Advancing Large Scale and Classroom Assessment through Research and Practice, San Antonio, TX, 26-30 April 2017 How to Cite?
AbstractThis study examines different ways of adjusting for skewness of the standardized log-likelihood statistic lZ in the cognitive diagnosis model framework. The performance of the adjusted person fit statistics is investigated using a simulation study. Results show that using the χ2-approximation correction yields better type I error rates and power.
DescriptionPaper Session, M7: Model fit issues with Diagnotic Classification Models
Persistent Identifierhttp://hdl.handle.net/10722/247981

 

DC FieldValueLanguage
dc.contributor.authorSantos, K-
dc.contributor.authorde la Torre, J-
dc.contributor.authorvon Davier, M-
dc.date.accessioned2017-10-18T08:35:52Z-
dc.date.available2017-10-18T08:35:52Z-
dc.date.issued2017-
dc.identifier.citationThe Annual Meeting of National Council on Measurement in Education (NCME): Advancing Large Scale and Classroom Assessment through Research and Practice, San Antonio, TX, 26-30 April 2017-
dc.identifier.urihttp://hdl.handle.net/10722/247981-
dc.descriptionPaper Session, M7: Model fit issues with Diagnotic Classification Models-
dc.description.abstractThis study examines different ways of adjusting for skewness of the standardized log-likelihood statistic lZ in the cognitive diagnosis model framework. The performance of the adjusted person fit statistics is investigated using a simulation study. Results show that using the χ2-approximation correction yields better type I error rates and power.-
dc.languageeng-
dc.relation.ispartofThe Annual Meeting of National Council on Measurement in Education-
dc.titleAdjusting person fit index for skewness in cognitive diagnosis modeling-
dc.typeConference_Paper-
dc.identifier.emailde la Torre, J: jdltorre@hku.hk-
dc.identifier.authorityde la Torre, J=rp02159-
dc.identifier.hkuros279628-
dc.publisher.placeSan Antonio, TX-

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