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Strasser, Nora – Journal of College Teaching & Learning, 2007
Avoiding statistical mistakes is important for educators at all levels. Basic concepts will help you to avoid making mistakes using statistics and to look at data with a critical eye. Statistical data is used at educational institutions for many purposes. It can be used to support budget requests, changes in educational philosophy, changes to…
Descriptors: Statistics, Statistical Data, Validity, Data Interpretation
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
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Wu, D. W. – International Journal of Mathematical Education in Science & Technology, 2006
The 2000 US presidential election between Al Gore and George W. Bush has been the most intriguing and controversial in American history. Using the Florida ballot data, Wu showed that the 2000 election result could have been reversed had the "butterfly ballot effect" been eliminated. Through a combinatorial approach, Harger concluded that…
Descriptors: United States History, Voting, Elections, Political Candidates
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Hinkle, Dennis E.; And Others – New Directions for Institutional Research, 1988
The data collected in higher education research are not always quantitative or continuous. Statistical methods using the log-linear model provide the institutional researcher with a powerful set of tools for addressing research questions when data are categorical. (Author/MSE)
Descriptors: Data Interpretation, Higher Education, Information Utilization, Institutional Research
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Harwell, Michael R. – Journal of Educational Statistics, 1992
A methodological framework is provided for quantitatively integrating Type I error rates and power values for Monte Carlo studies. An example is given using Monte Carlo studies of a test of equality of variances, and the importance of relating metanalytic results to exact statistical theory is emphasized. (SLD)
Descriptors: Computer Simulation, Data Interpretation, Mathematical Models, Meta Analysis
Morrisson, Christian; Murtin, Fabrice – Centre for the Economics of Education (NJ1), 2009
Global economic transformations have never been as dramatic as in the twentieth century. Most countries have experienced radical changes in the standards of income per capita, technology, fertility, mortality, income inequality and the extent of democracy in the course of the past century. It is the goal of many disciplines--economics, history,…
Descriptors: Economic Development, Educational Attainment, Demography, Global Approach
Wine, Jennifer S.; Whitmore, Roy W.; Heuer, Ruth E.; Biber, Melissa; Pratt, Daniel J. – 2000
This report describes the methods and procedures used for the full-scale data collection effort of the Beginning Postsecondary Students Longitudinal Study First Follow-Up 1996-98 (BPS:96/98). These students, who started their postsecondary education during the 1995-96 academic year, were first interviewed during 1996 as part of the National…
Descriptors: College Students, Comparative Analysis, Data Interpretation, Enrollment
Braveman, Paula; Bennett, Trude – 1993
The guide is based on project reports by the San Francisco Department of Public Health to improve and monitor perinatal health and children's health. These reports demonstrated the potential of information to help community advocacy groups, service providers, and program planners identify priorities for policy decisions and resource allocation.…
Descriptors: Advocacy, Child Advocacy, Child Health, Community Health Services