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This textbook introduces graduate students in education and policy research to data and statistical methods in state-level higher education policy analysis. It also serves as a methodological guide to students, practitioners, and researchers who want a clear approach to conducting higher education policy analysis that involves the use of institutional- and state-level secondary data and quantitative methods ranging from descriptive to advanced statistical techniques.
This book is unique in that it introduces readers to various types of data sources and quantitative methods utilized in policy research and in that it demonstrates how results of statistical analyses should be presented to higher education policy makers. It helps to bridge the gap between researchers, policy makers, and practitioners both within education policy and between other fields.
Coverage includes identifying pertinent data sources, the creation and management of customized data sets, teaching beginning and advanced statistical methods and analyses, and the presentation of analyses for different audiences (including higher education policy makers).
List of contents
1. Introduction.- 2. Asking and answering policy questions.- 3. Identifying data sources and creating custom datasets.- 4. Managing data.- 5. Getting to know "thy" data.- 6. Using descriptive statistics and graphs.- 7. Introducing intermediate statistical methods.- 8. Introducing advanced statistical methods.- 9. Presenting the analysis to higher education policy makers.- 10. Conclusion.
About the author
Dr. Marvin A. Titus’
research focuses on the economics and finance of higher education and quantitative methods. While he has explored how institutional and state finance influence student retention and graduation, Dr. Titus’ most recent work is centered on examining the determinants of institutional cost and productivity efficiency. He investigates how state higher education finance policies influence degree production. Through the use of a variety of econometric techniques, Dr. Titus is also exploring how state business cycles influence volatility in state funding of higher education. Named a TIAA Institute Fellow in 2018, Dr. Titus has published in top-tier research journals, including in the
Journal of Higher Education
,
Research in Higher Education
and
Review of Higher Education
. He is an associate editor of
Higher Education: Handbook of Theory and Research
, and has served on the editorial board of
Research in Higher Education
,
Review ofHigher Education
and the
Journal of Education Finance
. Dr. Titus also serves on several technical review panels for national surveys produced by the National Center for Education Statistics. To conduct his research utilizing national and customized state and institution-level datasets, Dr. Titus uses several statistical software packages such as Stata, Limdep, and HLM. He earned a BA in economics and history from York College of the City University of New York, an MA in economics from the University of Wisconsin-Milwaukee, and a PhD in higher education policy, planning, and administration from the University of Maryland.
Summary
This textbook introduces graduate students in education and policy research to data and statistical methods in state-level higher education policy analysis. It also serves as a methodological guide to students, practitioners, and researchers who want a clear approach to conducting higher education policy analysis that involves the use of institutional- and state-level secondary data and quantitative methods ranging from descriptive to advanced statistical techniques.
This book is unique in that it introduces readers to various types of data sources and quantitative methods utilized in policy research and in that it demonstrates how results of statistical analyses should be presented to higher education policy makers. It helps to bridge the gap between researchers, policy makers, and practitioners both within education policy and between other fields.
Coverage includes identifying pertinent data sources, the creation and management of customized data sets, teaching beginning and advanced statistical methods and analyses, and the presentation of analyses for different audiences (including higher education policy makers).
