Note: The deadline for applications for this Institute proposals has passed.
With funding from the National Science Foundation (NSF), the AERA Grants Program announces its AERA Institute on Statistical Analysis for Education Policy on Mathematics Education and Equity. The Institute's goal is to build the capacity of the U.S. education research community to use large-scale national and international data sets such as those from the National Center for Education Statistics (NCES), NSF, and other federal agencies for basic, policy, and applied research. Hands-on training is provided in the application of large-scale data sets, with special emphasis on using these data sets for policy-related research in education. In 2013, the Institute will focus on research questions related to mathematics education and equity. AERA Grants Governing Board members, agency staff, and outside experts jointly provide this training.
In 2013, the Institute will focus specifically on intersections of mathematics education and equity. Policy-relevant topics include such issues as students’ opportunities to learn specific mathematical content; access to high-quality mathematics teachers; students’ course-taking and career plans; and students’ mathematics achievement, beliefs, and attitudes. Patterns related to social class, race/ethnicity, language, gender, and disability will be considered.
The Institute will have three components: (1) discussion of current issues in mathematics education policy, practice, and equity; (2) an overview of some quasi-experimental methods appropriate to the analysis of large-scale data; and (3) opportunities to collaborate with peers on the application of federal data sets to the focal topic. More specifically, relevant examples of recent large-scale studies will be presented, and theoretical considerations related to the study of equity issues in mathematics education will be discussed. Participants will be briefly introduced to several useful quasi-experimental techniques, including regression discontinuity, propensity scores, and instrumental variables. Time will be allocated for both group and individual hands-on experiences to help participants apply the data set of their choice to relevant research questions.
Applicants should be familiar with commonly used statistical methods (e.g., regression) and either SPSS or Stata. Applicants must have a serious interest in the study of mathematics education and equity. Applicants should also be familiar with a large-scale data set that is appropriate to the study of mathematics education and equity. Participants will bring their choice of a relevant federal data set to the training. Publicly available national data sets such as, but not limited to, ECLS-K, ELS:2002, and HSLS:09, can be used for this training. Restricted data are not allowed (although knowledge gained at this Institute could later be applied to restricted datasets). Instruction will not focus on the particulars of specific data sets, but instead will focus on asking policy-relevant questions related to the focal topic and finding ways to address those questions by analyzing large-scale data sets.
A select group of scholars will be chosen to participate in the Institute. Those selected for participation will receive support covering the Institute's fees, transportation, housing, and meals for the dates of the Institute.
Dates and Location
May 20-23, 2013, in Washington, DC.
Director: Sarah Lubienski, University of Illinois at Urbana-Champaign
Elise Christopher, NCES
Dorothyjean Cratty, NCES
Jeremy Kilpatrick, University of Georgia
Joseph P. Robinson, University of Illinois at Urbana-Champaign
William H. Schmidt, Michigan State University
All applications should include:
Applicants and Review Criteria
Advanced graduate students and recent doctorates are especially encouraged to apply. Applications from historically underrepresented minority scholars are also strongly encouraged. Applicants may be U.S. citizens, U.S. permanent residents, or non-U.S. citizens working or studying at a U.S. institution. Please note that researchers who have attended the AERA Institute on Statistical Analysis for Education Policy in prior years are not eligible. Review criteria include: the applicant's statistical background at least to the intermediate level of multiple regression; computer literacy with knowledge of either SPSS or Stata; the applicant's experience in using a large-scale data set relevant to the research topic; substantive policy or practice interest in the focal research topic; and the Institute's fit with the applicant's career goals.