dc.contributor.author Guan Kung Saw en_US
dc.contributor.author Barbara Schneider en_US
dc.date.accessioned 2016-05-06T05:58:23Z
dc.date.available 2016-05-06T05:58:23Z
dc.date.issued 2015-12-??
dc.description.abstract School leaders and policy makers are often faced with serious challenges when determining the allocation of scarce resources for specific programs and practices. These decisions, typically made at the district, state or federal level, have become increasingly reliant on scientifically-based evidence that can inform what programs work, for whom, and under what conditions. To answer these questions researchers draw on a variety of methodological designs and statistical techniques to make robust inferences regarding the effect of relatively recent or existing programs and/or practices. The science of estimating effects has grown considerably over the past decade, aided in part by the availability of large-scale data sets that make it possible to simulate near-experimental conditions without employing traditional methods that require randomization of units (e.g., students, schools, districts) to treatment and control situations. These methods are particularly useful especially where randomization of subjects is not feasible. This article examines the opportunities and potential statistical problems when estimating effects with large-scale data sets for education policy and research. en_US
dc.identifier 20CFBE09-C5DC-77F7-81D6-C11CFEBEF100
dc.identifier.uri http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/78290
dc.language 英文
dc.publisher 教育研究與評鑑中心 zh_tw
dc.publisher Center for Educational Research and Evaluation en_US
dc.relation 23(4),93-119
dc.relation.ispartof 當代教育研究 zh_tw
dc.subject.other large-scale data en_US
dc.subject.other estimating effects en_US
dc.subject.other educational evaluations en_US
dc.title.alternative CHALLENGES AND OPPORTUNITIES FOR ESTIMATING EFFECTS WITH LARGE-SCALE EDUCATION DATA SETS zh_tw
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