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Rebeckah K. Fussell; Emily M. Stump; N. G. Holmes – Physical Review Physics Education Research, 2024
Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing…
Descriptors: Physics, Educational Researchers, Artificial Intelligence, Natural Language Processing
Brodersen, R. Marc; Gagnon, Douglas; Liu, Jing; Moss, Tony – Regional Educational Laboratory Central, 2021
This tool is intended to support state and local education agencies in developing a statistical model for estimating student postsecondary success at the school or district level. The tool guides education agency researchers, analysts, and decisionmakers through options to consider when developing their own model. The resulting model generates an…
Descriptors: Statistical Analysis, Models, Computation, Success
Jennifer Wine; Beth Hustedt; Jennifer Cooney; Erin Thomsen – National Center for Education Statistics, 2023
This report describes the design, methods, and results of the 2016/20 Baccalaureate and Beyond Longitudinal Study (B&B:16/20) conducted by the U.S. Department of Education's National Center for Education Statistics (NCES). It is the second follow-up with a cohort of bachelor's degree recipients originally identified during the 2015-16 National…
Descriptors: Longitudinal Studies, College Graduates, Bachelors Degrees, College Students
Swank, Jacqueline M.; Mullen, Patrick R. – Measurement and Evaluation in Counseling and Development, 2017
The article serves as a guide for researchers in developing evidence of validity using bivariate correlations, specifically construct validity. The authors outline the steps for calculating and interpreting bivariate correlations. Additionally, they provide an illustrative example and discuss the implications.
Descriptors: Correlation, Construct Validity, Guidelines, Data Interpretation
Porter, Kristin E. – Journal of Research on Educational Effectiveness, 2018
Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) are statistical…
Descriptors: Statistical Analysis, Program Effectiveness, Intervention, Hypothesis Testing
Porter, Kristin E. – Grantee Submission, 2017
Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) are statistical…
Descriptors: Statistical Analysis, Program Effectiveness, Intervention, Hypothesis Testing
Raudenbush, Stephen W.; Bloom, Howard S. – MDRC, 2015
The present paper, which is intended for a diverse audience of evaluation researchers, applied social scientists, and research funders, provides a broad overview of the conceptual and statistical issues involved in using multisite randomized trials to learn "about" and "from" variation in program effects across…
Descriptors: Program Effectiveness, Research Methodology, Statistical Analysis, Differences
Porter, Kristin E. – MDRC, 2016
In education research and in many other fields, researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple…
Descriptors: Statistical Analysis, Program Effectiveness, Intervention, Hypothesis Testing
Valliant, Richard; Dever, Jill A.; Kreuter, Frauke – Springer, 2013
Survey sampling is fundamentally an applied field. The goal in this book is to put an array of tools at the fingertips of practitioners by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This book serves at least…
Descriptors: Sampling, Surveys, Computer Software, College Students
Ellis, Paul D. – Cambridge University Press, 2010
This succinct and jargon-free introduction to effect sizes gives students and researchers the tools they need to interpret the practical significance of their results. Using a class-tested approach that includes numerous examples and step-by-step exercises, it introduces and explains three of the most important issues relating to the practical…
Descriptors: Effect Size, Statistical Analysis, Meta Analysis, Research
Schochet, Peter Z.; Puma, Mike; Deke, John – National Center for Education Evaluation and Regional Assistance, 2014
This report summarizes the complex research literature on quantitative methods for assessing how impacts of educational interventions on instructional practices and student learning differ across students, educators, and schools. It also provides technical guidance about the use and interpretation of these methods. The research topics addressed…
Descriptors: Statistical Analysis, Evaluation Methods, Educational Research, Intervention

Shukla, Shyam S.; Rusling, James F. – Analytical Chemistry, 1984
Discusses how computational errors arise in analysis of data and how they can be minimized. Shows that all computations are subject to roundoff/truncation errors and how such errors are propagated and influence the stability and condition of a reaction. Applications to procedures used in analytical chemistry are addressed. (JN)
Descriptors: Chemical Analysis, Chemistry, College Science, Computation