Some statistical models used in education research are complex. This complexity arises in part because the data structures that underlie these statistical models involve multiple nested (i.e., cross-classified multilevel models) and non-nested groupings (i.e., partially-nested designs). Another source of complexity in these models results from the fact that key variables, such as student achievement, can be measured only indirectly and are represented in the model by latent variables.
Through this program, the Institute seeks to improve the quality of education for all students - prekindergarten through postsecondary and adult education - by advancing the understanding of and practices for teaching, learning, and organizing education systems. By identifying what works, what doesn't, and why, the goal of this research grant program is to improve educational outcomes for all students, particularly those at risk of failure.
Through the grant program on Statistical and Research Methodology in Education (Methods), the Institute supports research to advance education research methods and statistical analyses. The long-term outcome of this research program will be a wide range of methodological and statistical products that will better enable education scientists to conduct rigorous education research.
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