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, IES 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. Stats/Methods grants are divided into Core (previously Regular) and Early Career. Core grants last a maximum of 3 years, with a maximum award amount of $900,000 to support the development of new and improved statistical and research methods and their dissemination to education researchers or $350,000 to support the compilation of existing research and information for a given method into toolkits, guidelines, compendia, and review papers that help education researchers understand and apply the method. Early Career grants last a maximum of 2 years, with a maximum award amount of $300,000 to support the development of new and improved statistical and research methods by early career researchers.
Stats/Methods debuted as a separate grant program in fiscal year (FY) 2009. IES's history of supporting Stats/Methods research, however, began prior to FY 2009, with 14 methodological projects being funded through IES's Unsolicited grant mechanism. The distinction between Regular (now Core) and Early Career applications was introduced in the FY 2014 Stats/Methods 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 in a context that was set off from the applications submitted by well-established methodologists. Thirteen Early Career grants have been awarded to date. Beginning in FY 2022, Regular grants were renamed Core grants and expanded to respond to the need in the field for methodologists to condense existing methodological approaches and software into dissemination products that are even more user-friendly for applied researchers, such as toolkits and usage guidelines.
Why is Stats/Methods Unique?
Many grant competitions held by IES are intended to support research that directly affects students and/or teachers in the classroom. The field, however, 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 rarely involve collecting data from people. 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.
The methodological research supported by IES has made useful contributions to the field, including:
- A substantial body of research spanning more than a decade on the use of value-added modeling in education;
- Software that allows researchers to obtain intraclass correlations for math and science tests in school districts by grade level;
- Further development of the HLM software package, particularly its capacity to handle missing data imputation in multilevel models;
- Advances in the application of inferential statistics to single-case data and development of effect size indices for single-case research;
- Expansion of regression discontinuity designs and the software available to analyze data from these designs;
- The creation of xxM, a software package for advanced structural equation modeling, multilevel modeling, and multilevel structural equation modeling.
The 33 current Stats/Methods grants (including 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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