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dc.contributor.authorPierre Foyen_US
dc.contributor.authorLiqun Yinen_US
dc.date.accessioned2019-08-12T04:59:02Z
dc.date.available2019-08-12T04:59:02Z
dc.date.issued2015-12-??
dc.description.abstractLarge-scale assessments in education, such as IEA’s TIMSS and PIRLS, rely on sophisticated assessment instruments, elaborate sample designs, and leading-edge item response theory to meet their analytical objectives. Both assessments provide a rich and complex database intended to support and promote secondary analyses. This paper describes the complex international database structures and the statistical methods and procedures for analyzing TIMSS and PIRLS data, with examples provided for illustration. Three essential elements must be considered by researchers in any statistical analysis based on data from the TIMSS and PIRLS international databases. The first is the use of sampling weights in order to produce accurate and reliable results. Second, both assessments apply the Jackknife Repeated Replication technique to derive proper estimates of sampling variance. Finally, with student achievement reported as sets of five plausible values, statistical analyses are performed five times, once for each plausible value, and the final results aggregated across the five plausible values. Researchers and users of the TIMSS and PIRLS international databases who conduct their analyses as described in this paper should feel confident in the results their analyses will yield.en_US
dc.identifier53C061E9-CB6F-DF70-CD8C-002FB89210FB
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/81275
dc.language英文
dc.publisher教育研究與評鑑中心zh_tw
dc.publisherCenter for Educational Research and Evaluationen_US
dc.relation23(4),41-61
dc.relation.ispartof當代教育研究zh_tw
dc.subject.otheritem response theoryen_US
dc.subject.otherjackknife repeated replicationen_US
dc.subject.othermatrix samplingen_US
dc.subject.othermulti-stage cluster samplingen_US
dc.subject.otherplausible valuesen_US
dc.subject.otherPIRLSen_US
dc.subject.otherTIMSSen_US
dc.title.alternativeDATA ANALYSES WITH IEA’S TIMSS AND PIRLS INTERNATIONAL DATABASESzh_tw

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