With funding from the National Science Foundation (NSF), the AERA Grants Program announces the AERA Faculty Institute for the Teaching of Statistics with Large-Scale Data Sets. The Faculty Institute's goal is to help develop a critical mass of U.S. education researchers at doctoral granting institutions using large-scale federal data sets, especially those sponsored by the National Center for Education Statistics (NCES), NSF, and other federal agencies. These data sets, which are often longitudinal and nationally representative, offer an excellent opportunity for students and early career scholars to conduct research and learn advanced quantitative methods with high quality policy-relevant data. Secondary data analysis of federal data sets provides one of the most opportune and cost-effective ways of generating knowledge and contributing to policy deliberations based on large numbers of individuals and observations. This Institute aims to "train the trainers," enabling more education researchers to take advantage of these rich data resources. Historically underrepresented minority faculty are strongly encouraged to apply.

Eligibility
This training is for faculty members at U.S. universities with doctoral programs who teach basic introductory doctoral-level statistics or methodology courses and who seek to integrate large-scale federal education data sets into the teaching of these courses. Applicants may be faculty members in graduate schools or departments of education, or from other disciplines with an interest in education research. Prior experience using large-scale data sets is required. Applicants must be familiar with and able to work with a publicly available federal data set (such as those from NCES) of the applicant's choice. Attendees will be expected to bring the relevant data set to the Institute.

Description
The AERA Faculty Institute will focus on how to incorporate secondary data analysis, especially the use of large-scale federal data sets, into doctoral-level teaching of statistics or methodology courses. The Faculty Institute will focus on how to use large-scale data sets for teaching concepts central to basic statistics courses. The concepts to be covered include: estimation, standard errors and sampling distributions; reliability; longitudinal analysis; and treatment of missing data. During the training, participants will develop teaching modules based on these topics appropriate for incorporation into their courses. Participants should come away with the knowledge and resources necessary to teach the statistical methods learned in the training to doctoral-level students at their home institutions.

Participant Fellowships
A select group of faculty will be chosen to participate in the Faculty 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
June 10-13, 2013; Location TBD

Institute Personnel
Director
William Schmidt, Michigan State University

Faculty
Tasha Beretvas, University of Texas - Austin
Richard Houang, Michigan State University
Other Faculty TBA

Application Requirements
All applications should include:

1)  Biographical Information
2)  Statement of Interest in PDF (maximum two-pages, single-spaced) briefly describing the applicant's background, teaching experience, teaching and career goals, and how the applicant would benefit from the Institute. Priority will be given to those applicants who are committed to using large-scale data sets for the teaching of doctoral-level beginning statistics or methodology courses.
3)  Summarized Curriculum Vitae in PDF (maximum one-page) listing education and employment history, relevant graduate courses, and publications.
4)  Letter of support (submitted via email to [email protected]) from the applicant's Dean or Department Chair stating that the applicant will be scheduled to teach a course within a year that could include the information and skills learned in the Faculty Institute.

Review criteria include: the applicant's statistical background and teaching record; computer literacy with knowledge of current statistical software package(s); interest in using large-scale data sets in teaching; likelihood of the methods presented during the Institute being incorporated into a course during the next year; and the Institute's fit with the applicant's teaching and career goals.

Application Submission
The deadline for applications is 11:59pm Pacific Time on Thursday, February 21, 2013. Applications must be submitted electronically to AERA via the online submission tool. Incomplete submissions will not be considered. Final decisions will be made in early Spring 2013. All awards are contingent upon AERA's receiving continued federal funding. If you have any questions, please contact Kevin Dieterle at [email protected] or 202-238-3227.