Show simple item record

dc.contributor.authorChungbaek, Youngyunen_US
dc.date.accessioned2017-04-06T15:42:34Z
dc.date.available2017-04-06T15:42:34Z
dc.date.issued2011-05-02en_US
dc.identifier.otheretd-05162011-110459en_US
dc.identifier.urihttp://hdl.handle.net/10919/77086
dc.description.abstractThis study involves investigating the impacts of ignoring nested data structure in Rasch/1PL item response theory (IRT) model via a two-level and three-level hierarchical generalized linear model (HGLM). Currently, Rasch/IRT models are frequently used in educational and psychometric researches for data obtained from multistage cluster samplings, which are more likely to violate the assumption of independent observations of examinees required by Rasch/IRT models. The violation of the assumption of independent observation, however, is ignored in the current standard practices which apply the standard Rasch/IRT for the large scale testing data. A simulation study (Study Two) was conducted to address this issue of the effects of ignoring nested data structure in Rasch/IRT models under various conditions, following a simulation study (Study One) to compare the performances of three methods, such as Penalized Quasi-Likelihood (PQL), Laplace approximation, and Adaptive Gaussian Quadrature (AGQ), commonly used in HGLM in terms of accuracy and efficiency in estimating parameters. As expected, PQL tended to produce seriously biased item difficulty estimates and ability variance estimates whereas almost unbiased for Laplace or AGQ for both 2-level and 3-level analysis. As for the root mean squared errors (RMSE), three methods performed without substantive differences for item difficulty estimates and ability variance estimates in both 2-level and 3-level analysis, except for level-2 ability variance estimates in 3-level analysis. Generally, Laplace and AGQ performed similarly well in terms of bias and RMSE of parameter estimates; however, Laplace exhibited a much lower convergence rate than that of AGQ in 3-level analyses. The results from AGQ, which produced the most accurate and stable results among three computational methods, demonstrated that the theoretical standard errors (SE), i.e., asymptotic information-based SEs, were underestimated by at most 34% when 2-level analyses were used for the data generated from 3-level model, implying that the Type I error rate would be inflated when the nested data structures are ignored in Rasch/IRT models. The underestimated theoretical standard errors were substantively more severe as the true ability variance increased or the number of students within schools increased regardless of test length or the number of schools.
dc.language.isoen_USen_US
dc.publisherVirginia Techen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjecttheoretical standard error.en_US
dc.subjectRMSEen_US
dc.subjectMCSEen_US
dc.subjectbiasen_US
dc.subjectAGQen_US
dc.subjectLaplaceen_US
dc.subjectPQLen_US
dc.subjectIRTen_US
dc.subjectRaschen_US
dc.subjectHGLMen_US
dc.subjectnested data structureen_US
dc.subjectMonte Carlo simulationen_US
dc.titleImpacts of Ignoring Nested Data Structure in Rasch/IRT Model and Comparison of Different Estimation Methodsen_US
dc.typeDissertationen_US
dc.contributor.departmentEducational Leadership and Policy Studiesen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairMiyazaki, Yasuoen_US
dc.contributor.committeememberHein, Serge F.en_US
dc.contributor.committeememberChang, Midoen_US
dc.contributor.committeememberSkaggs, Gary E.en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://theses.lib.vt.edu/theses/available/etd-05162011-110459/en_US
dc.date.sdate2011-05-16en_US
dc.date.rdate2016-09-27
dc.date.adate2011-06-06en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record