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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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Gibbs, Benjamin G.; Shafer, Kevin; Miles, Aaron – International Journal of Research & Method in Education, 2017
While the use of inferential statistics is a nearly universal practice in the social sciences, there are instances where its application is unnecessary and potentially misleading. This is true for a portion of research using administrative data in educational research in the United States. Surveying all research articles using administrative data…
Descriptors: Statistical Inference, Statistics, Data, Information Utilization
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Lodge, Jason M.; Alhadad, Sakinah S. J.; Lewis, Melinda J.; Gaševic, Dragan – Technology, Knowledge and Learning, 2017
The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate…
Descriptors: Statistical Inference, Data Interpretation, Interdisciplinary Approach, Higher Education
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Drummond, Gordon B.; Vowler, Sarah L. – Advances in Physiology Education, 2011
Experimental data are analysed statistically to allow researchers to draw conclusions from a limited set of measurements. The hard fact is that researchers can never be certain that measurements from a sample will exactly reflect the properties of the entire group of possible candidates available to be studied (although using a sample is often the…
Descriptors: Educational Research, Statistical Inference, Data Interpretation, Probability
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Sun, Shuyan; Pan, Wei; Wang, Lihshing Leigh – Journal of Educational Psychology, 2010
Null hypothesis significance testing has dominated quantitative research in education and psychology. However, the statistical significance of a test as indicated by a p-value does not speak to the practical significance of the study. Thus, reporting effect size to supplement p-value is highly recommended by scholars, journal editors, and academic…
Descriptors: Effect Size, Statistical Inference, Statistical Significance, Data Interpretation
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Vacha-Haase, Tammi; Nilsson, Johanna E. – Measurement and Evaluation in Counseling and Development, 1998
Statistical significance reporting and use in educational and psychological research is reviewed. An assessment of the use of statistical significance in articles published in MECD from 1990-1996 is presented. The elements of statistical significance (including sample size, effect size, and power), interpretation of results, common erroneous…
Descriptors: Data Interpretation, Educational Research, Hypothesis Testing, Measurement
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Knapp, Thomas R. – Mid-Western Educational Researcher, 1999
Presents an opinion on the appropriate use of significance tests, especially in the context of regression analysis, the most commonly encountered statistical technique in education and related disciplines. Briefly discusses the appropriate use of power analysis. Contains 47 references. (Author/SV)
Descriptors: Data Interpretation, Educational Research, Effect Size, Hypothesis Testing