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Migliavaca, Celina Borges; Stein, Cinara; Colpani, Verônica; Barker, Timothy Hugh; Ziegelmann, Patricia Klarmann; Munn, Zachary; Falavigna, Maicon – Research Synthesis Methods, 2022
Over the last decade, there has been a 10-fold increase in the number of published systematic reviews of prevalence. In meta-analyses of prevalence, the summary estimate represents an average prevalence from included studies. This estimate is truly informative only if there is no substantial heterogeneity among the different contexts being pooled.…
Descriptors: Incidence, Meta Analysis, Statistics, Statistical Distributions
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Sandra Jo Wilson; Brian Freeman; E. C. Hedberg – Grantee Submission, 2024
As reporting of effect sizes in evaluation studies has proliferated, researchers and consumers of research need tools for interpreting or benchmarking the magnitude of those effect sizes that are relevant to the intervention, target population, and outcome measure being considered. Similarly, researchers planning education studies with social and…
Descriptors: Benchmarking, Effect Size, Meta Analysis, Statistical Analysis
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Deke, John; Finucane, Mariel; Thal, Daniel – National Center for Education Evaluation and Regional Assistance, 2022
BASIE is a framework for interpreting impact estimates from evaluations. It is an alternative to null hypothesis significance testing. This guide walks researchers through the key steps of applying BASIE, including selecting prior evidence, reporting impact estimates, interpreting impact estimates, and conducting sensitivity analyses. The guide…
Descriptors: Bayesian Statistics, Educational Research, Data Interpretation, Hypothesis Testing
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Schild, Anne H. E.; Voracek, Martin – Research Synthesis Methods, 2015
Research has shown that forest plots are a gold standard in the visualization of meta-analytic results. However, research on the general interpretation of forest plots and the role of researchers' meta-analysis experience and field of study is still unavailable. Additionally, the traditional display of effect sizes, confidence intervals, and…
Descriptors: Graphs, Visualization, Meta Analysis, Data Interpretation
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Carter, Nancy; Felton, Nathan; Schwertman, Neil – Journal of Statistics Education, 2014
Engaging students in active learning can enhance their understanding and appreciation of a subject such as statistics. Classroom activities and projects help to engage students and further promote the learning process. In this paper, an activity investigating the influence of population size and wealth on the medal counts from the 2012 London…
Descriptors: Class Activities, Demography, Athletics, Awards
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Martínez Abad, Fernando; Chaparro Caso López, Alicia A. – School Effectiveness and School Improvement, 2017
In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…
Descriptors: Foreign Countries, Data Collection, Statistical Analysis, Evaluation Methods
Petrilli, Michael J.; Scull, Janie – Thomas B. Fordham Institute, 2011
The release of the latest results of the Programme for International Student Assessment (PISA) garnered all the usual headlines about America's lackluster performance and the rise of competitor nations. To be sure, the findings--that America's fifteen-year-olds perform in the middle of the pack in both reading and math--are disconcerting for a…
Descriptors: Low Achievement, Academic Achievement, Achievement Rating, Achievement Tests
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Douzenis, Cordelia; Rakow, Ernest A. – 1987
Outliers, extreme data values relative to others in a sample, may distort statistics that assume internal levels of measurement and normal distribution. The outlier may be a valid value or an error. Several procedures are available for identifying outliers, and each may be applied to errors of prediction from the regression lines for utility in a…
Descriptors: Correlation, Data Analysis, Data Interpretation, Statistical Analysis
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Krishnan, Parmeswara – Alberta Journal of Educational Research, 1995
Comments on some methodological limitations of the research base of "The Bell Curve": blind use of the normal distribution (bell curve); avoidance of nonnormal statistical distributions, which are more appropriate for some social and economic characteristics; copious use of percentiles and quintiles, inappropriate with nonnormal…
Descriptors: Data Interpretation, Intelligence Quotient, Multivariate Analysis, Research Methodology
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Beck, E. M.; Tolnay, Stewart E. – Historical Methods, 1995
Asserts that traditional approaches to multivariate analysis, including standard linear regression techniques, ignore the special character of count data. Explicates three suitable alternatives to standard regression techniques, a simple Poisson regression, a modified Poisson regression, and a negative binomial model. (MJP)
Descriptors: Data Interpretation, Evaluation Criteria, Higher Education, Multivariate Analysis