Guan Kung Saw; Barbara Schneider

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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當代教育研究