Introduction to JEBS Special Issue on NAEP Linked Aggregate Scores

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Journal of Educational and Behavioral Statistics Pub Date : 2021-04-01 DOI:10.3102/10769986211001480

D. McCaffrey, S. Culpepper

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引用次数: 1

Abstract

The Stanford Education Data Archive (SEDA) was created by Sean Reardon, Andrew Ho, Demetra Kalogrides, and their colleagues using annual state summative test score data retrieved from the EDFacts Restricted-Use Files and publicly available NAEP data from the National Center for Education Statistics. SEDA provides test score data on a common scale across all states for mathematics and reading language arts for students in Grades 3 through 8 for almost all schools, districts, and counties in the United States. An online tool (edopportu nity.org) allows users to visually compare schools and districts from anywhere in the country. Data also include various covariates at each of these levels, and all the data can be downloaded for free for analysis. These data have the potential to be a very valuable resource for researchers, educators, policy makers, and possibly even the general public. The catch is that there is no common standardized test administered to students in Grades 3 through 8 in all schools and school districts in all states. NAEP is only administered in a relatively small sample of schools in each state and only to students in Grades 4 and 8 and only every other year. The school data in SEDA are derived from the annual tests administered by each state in accordance with federal regulations. Reardon, Ho, Kalogrides, and colleagues start with aggregate data of the numbers of students in each school or district meeting various performance levels on their state standardized tests. State tests are on different scales and test somewhat different content. They also use different cutoffs for performance levels that are not common across states. Reardon, Ho, Kalogrides, and colleagues convert these frequencies to means and standard deviations for the scores in each school or district using the Heteroskedastic Ordered Probit model that was developed into a series of papers in JEBS (Lockwood et al., 2018; Reardon et al., 2017; Shear & Reardon, 2021). They then link these means and standard deviations to the NAEP scale using methods described in Reardon et al. (2021). Reardon, Ho, Kalogrides, and colleagues stitched together a collection of methods to create a national data source of Journal of Educational and Behavioral Statistics 2021, Vol. 46, No. 2, pp. 135–137 DOI: 10.3102/10769986211001480 Article reuse guidelines: sagepub.com/journals-permissions © 2021 AERA. https://journals.sagepub.com/home/jeb

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关于NAEP关联总分的JEBS特刊简介

斯坦福教育数据档案(SEDA)是由Sean Reardon、Andrew Ho、Demetra Kalogrides和他们的同事创建的，他们使用从EDFacts限制使用文件中检索的年度州总结性考试成绩数据和国家教育统计中心公开的NAEP数据。SEDA为美国几乎所有学校、地区和县的3至8年级学生提供通用规模的数学和阅读语言艺术考试成绩数据。一个在线工具(edopportunities nity.org)可以让用户直观地比较全国各地的学校和学区。数据还包括这些级别上的各种协变量，所有数据都可以免费下载以供分析。这些数据有可能成为研究人员、教育工作者、政策制定者，甚至可能是公众非常宝贵的资源。问题是，在所有州的所有学校和学区，没有针对三年级到八年级学生的统一标准化考试。NAEP只在每个州相对较小的学校样本中实施，只针对四年级和八年级的学生，而且每隔一年才实施一次。SEDA中的学校数据来自各州根据联邦法规进行的年度测试。Reardon、Ho、Kalogrides和同事们从每个学校或学区在州标准化考试中达到不同表现水平的学生人数的汇总数据开始。各州考试的规模不同，测试的内容也有所不同。他们还使用不同的绩效水平截止值，这在各州并不常见。Reardon、Ho、Kalogrides和同事使用异方差有序概率模型(Heteroskedastic Ordered Probit model)将这些频率转换为每个学校或学区分数的均值和标准差，该模型已在JEBS上发表了一系列论文(Lockwood等人，2018;Reardon et al.， 2017;Shear & Reardon, 2021)。然后，他们使用Reardon等人(2021)中描述的方法将这些均值和标准差与NAEP量表联系起来。Reardon, Ho, Kalogrides和同事们将一系列方法结合在一起，创建了《教育与行为统计杂志》2021年第46卷第2期135-137页的国家数据源DOI: 10.3102/10769986211001480文章重用指南:sagepub.com/journals-permissions©2021 AERA。https://journals.sagepub.com/home/jeb

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来源期刊

Journal of Educational and Behavioral Statistics Multiple-

CiteScore

4.40

自引率

4.20%

发文量

期刊介绍： Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.