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Statistical and Research Methodology in Education–Early Career
Grantees
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Investigator
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Goals
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FY Awards
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Dr. Phill Gagné
(202) 245-7139
Phill.Gagne@ed.gov
REQUEST FOR APPLICATIONS: 
FY 2019 84.305D (PDF: 791 KB)
The Statistical and Research Methodology in Education -Early Career grant program supports research to advance education research methods and statistical analyses so that, in the long-term, there will be a wide-range of methodological and statistical tools and techniques that will enable education scientists to conduct rigorous education research. The Early Career grant program requires the principal investigator to be a recent Ph.D. recipient and provides grants funds for up to 2 years.
The goal of the Statistical and Research Methodology in Education (Stats/Methods) grant program (CFDA 84.305D) is to provide a wide range of methodological and statistical tools that will better enable applied education scientists to conduct rigorous education research. Through this grant program, the Institute invites applications to develop new methodological approaches, to extend and improve existing methods, and to create other tools that can enhance the ability of researchers to conduct high quality, scientific education research. Applicants to the Stats/Methods program who received their doctoral degrees within the last 5 years can submit an Early Career Stats/Methods application for a grant lasting a maximum of 2 years, with a maximum award amount of $200,000.
PORTFOLIO SUMMARY
Between 2014–2016, NCER has invested nearly $1.8 million in the Stats/Methods Early Career grant program to support nine research projects.
HISTORY/BACKGROUND
The Stats/Methods grant program debuted as an official grant program in fiscal year (FY) 2009 and the Stats/Method Early Career grant program began in 2014. IES’s history of supporting Stats/Methods research, however, began prior to FY 2009, with 14 methodological applications being funded through IES’s Unsolicited grant mechanism. There was no distinction in the Stats/Methods program between Regular and Early Career applications until the FY 2014 competition. For that competition, the new category of Early Career was introduced in order to give early career methodologists an opportunity to apply for a smaller grant (approximately $200,000 funding for 18 months to 2 years) in a context that was set off from the applications submitted by well-established methodologists. There have been nine Statistical and Methodology in Education-Early Career grants:
Why is Stats/Methods-Early Career Unique?
Most grant competitions held by IES are intended to support research that directly affects students and/or teachers in the classroom. The field also needs to advance in terms of how research is conducted. Through the Stats/Methods program, grantees conduct research that helps applied researchers address challenges often encountered in education research, such as missing data, measurement error, small samples, the clustered nature of most group-design research, and the frequent phase shifts typically found in single-case research. Stats/Methods grants are also unique in that they do not generally involve collecting data from people. Instead, Stats/Methods grants rely on Monte Carlo simulations, mathematical derivations, and the creation of new software to refine and develop designs and analyses which applied researchers use for collecting, analyzing, and interpreting data from education settings. Early Career grants address the same sorts of research questions and use the same types of designs as Regular grants. The shorter duration and lower budget amount for Early Career grants makes them ideal for relatively new professionals, who are working on developing their grant experience and establishing themselves as experts in methodological research.
Implications
The nine current Stats/Methods Early Career grants are investigating a broad array of important topics, including estimation of treatment effect heterogeneity, additional advances in missing data imputation, continued development of effect size indices for single-case research, development of advanced IRT techniques which include projects with potential implications for NAEP, and advances in software for computing statistical power.
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