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Conaway, Carrie; Goldhaber, Dan – Center for Education Data & Research, 2018
A key job of education policymakers is to make decisions under uncertainty. They must weigh the risks, rewards, and costs of different interventions, policies, and mixes of resources, and make decisions even when the likely outcome is uncertain. How policymakers think about and deal with uncertainty has important implications for resource…
Descriptors: Educational Policy, Decision Making, Standards, Evidence
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Gorard, Stephen; Gorard, Jonathan – International Journal of Social Research Methodology, 2016
This brief paper introduces a new approach to assessing the trustworthiness of research comparisons when expressed numerically. The 'number needed to disturb' a research finding would be the number of counterfactual values that can be added to the smallest arm of any comparison before the difference or 'effect' size disappears, minus the number of…
Descriptors: Statistical Significance, Testing, Sampling, Attrition (Research Studies)
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Seastrom, Marilyn – Institute of Education Sciences, 2017
The Every Student Succeeds Act (ESSA) of 2015 (Public Law 114-95) requires each state to create a plan for its statewide accountability system. In particular, ESSA calls for state plans that include strategies for reporting education outcomes by grade for all students and for economically disadvantaged students, students from major racial and…
Descriptors: Accountability, Best Practices, Information Security, Sample Size
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Newman, Carole; Newman, Isadore – Teacher Educator, 2013
The concept of teacher accountability assumes teachers will use data-driven decision making to plan and deliver appropriate and effective instruction to their students. In order to do so, teachers must be able to accurately interpret the data that is given to them, and that requires the knowledge of some basic concepts of assessment and…
Descriptors: Decision Making, Basic Vocabulary, Data, Accountability
Onwuegbuzie, Anthony J.; Daniel, Larry G.; Roberts, J. Kyle – 2001
The purpose of this paper is to illustrate how displaying disattenuated correlation coefficients along with their unadjusted counterparts will allow the reader to assess the impact of unreliability on each bivariate relationship. The paper also demonstrates how a proposed new "what if reliability" analysis can complement the conventional null…
Descriptors: Correlation, Reliability, Sample Size, Statistical Significance
Onwuegbuzie, Anthony J.; Daniel, Larry G. – 2000
The purposes of this paper are to identify common errors made by researchers when dealing with reliability coefficients and to outline best practices for reporting and interpreting reliability coefficients. Common errors that researchers make are: (1) stating that the instruments are reliable; (2) incorrectly interpreting correlation coefficients;…
Descriptors: Correlation, Generalization, Reliability, Research Methodology
McDermott, Paul A.; Watkins, Marley W. – 1979
A computer program named Program STANDARD is presented and demonstrated. This program calculates the statistical significance of the overall agreement of the categorical assignments. The program is based on Light's statistic, G, for describing the conjoint agreement of many observers with correct or standard set of classifications on nominal…
Descriptors: Classification, Computer Programs, Goodness of Fit, Nonparametric Statistics
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Suen, Hoi K.; Stevens, Robert J. – American Journal of Distance Education, 1993
Reports on a review of empirical research reports submitted over the past several years to the "American Journal of Distance Education" that identified common analytic problems and errors often overlooked by distance education researchers. Topics discussed include significance testing; assessment issues, including reliability and…
Descriptors: Distance Education, Educational Research, Evaluation Methods, Evaluation Problems
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Cohen, Patricia – Evaluation and Program Planning: An International Journal, 1982
The various costs of Type I and Type II errors of inference from data are discussed. Six methods for minimizing each error type are presented, which may be employed even after data collection for Type I and which minimizes Type II errors by a study design and analytical means combination. (Author/CM)
Descriptors: Analysis of Variance, Data Analysis, Data Collection, Error of Measurement