Higher Education Policy Analysis Using Quantitative Techniques: Data, Methods and Presentation
reviewed by Gloria Crisp & Michelle Bump - October 18, 2021
Title: Higher Education Policy Analysis Using Quantitative Techniques: Data, Methods and Presentation
Author(s): Marvin Titus
Publisher: Springer Publishing, New York
ISBN: 3030608301, Pages: 249, Year: 2021
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Marvin Titus, associate professor of higher education at the University of Maryland, College Park blends his expertise in economics and finance and experience in higher education policy into a concise reference material in Higher Education Policy Analysis Using Quantitative Techniques: Data Methods and Presentations. This book is part oaf a series on Quantitative Methods in the Humanities and Social Sciences published by Springer. Professor Titus explains that the purpose of the book is to make policy research relevant and accessible for graduate students, policy analysts, academic researchers, and policymakers. He accomplishes this purpose by outlining how to ask policy questions and providing guidance to researchers in deciding when and why to use various approaches to address such questions. The first half of the book is focused on framing policy questions, identifying data sources, and creating and managing datasets. The second half of the text reviews statistical techniques that can be used for policy questions, including intermediate and advanced quantitative techniques. The text concludes with an overview of how to present data analyses to policymakers.
The 10 chapters in this book are organized to follow the research process. After an introduction, Chapter 1 explains how to ask the right policy questions, including what and how questions. Policy analysis is unique in that only some changes can be made through policy; therefore, its important to identify variables that can be affected by policy changes while controlling for variables that cannot be affected by policy changes. Chapter 2 discusses ways researchers and policy analysts should be forward-thinking, including predicting and anticipating follow-up questions from policymakers. Chapter 3 provides a detailed overview of datasets currently available for policy research, including international, national, state, and institutional level data. Graduate students looking to identify data sources for thesis or dissertation work will find this chapter particularly useful, as many free and publicly available datasets are identified and described in this chapter. In Chapter 4, Titus describes how to create and manage datasets using Excel workbooks and Stata statistical software. Notably, he provides step-by-step instructions for researchers to develop and manage both primary and secondary datasets as well as links to datasets that readers can download to practice formatting. The focus of Chapter 5, Getting to Know Thy Data, is to understand and compensate for common limitations of primary and secondary data. In this chapter, Titus takes readers through the steps of describing and cleaning data, including identifying and handling missing data in Stata. Detailed steps and examples are again provided, giving readers the opportunity to learn from seeing example Stata code and output. Unlike most similar research texts, Titus gives consideration to distinguishing between using data that are missing at random (MAR) and data that are missing completely at random (MCAR).
Chapter 6 moves the reader into how to run policy-focused exploratory data analysis. Titus devotes this chapter to the use of descriptive statistics to conduct data analyses with specific attention toward using different types of graphs. He suggests that basic data analyses are valuable in that policymakers may rely primarily on descriptive findings and graphs. These analyses can be used for communication with policymakers and as a foundation for more advanced statistical analyses. Chapter 7 describes the use of regression techniques for correlational studies and multivariate regression techniques that policy researchers can use to analyze nested data. Titus begins with an overview of how and when to use OLS, fixed-effects, and random effects regression, and then shows readers how to modify these techniques to infer causal effects using difference-in-difference (DiD) estimators. Chapters 8 and 9 then detail how and when to use advanced correlational techniques when the assumptions of ordinary least squares (OLS) regression cannot be met (Chapter 8) and when researchers have macro panel data (Chapter 9). In our experience, much of the content of these two chapters is often overlooked in educational research and policy textbooks (e.g., autocorrelation tests), making these chapters uniquely useful. Similarly, readers will find tremendous value in Chapter 10 as it includes information about how to present analyses for presentation to policymakersincluding visually summarizing key findings using tables, graphs, and maps. The chapter begins by detailing how to present descriptive statistics using Microsoft Word Tables (readers may be interested to learn that tables can be automatically created using a Stata user-written module). In addition to explaining how to use graphs, Titus gives space in this final chapter to explaining how to create choropleth maps, which we feel is an underutilized visual tool in higher education policy research.
As a second-year doctoral student and more experienced researcher, we both appreciated the clear and logical organization of the book. In many ways the text reads like an instruction manual, providing directions, tools, and examples researchers can use and apply to conducting their own policy-oriented research. Titus has significant experience in finance-oriented policy analysis, and this is evident from the skillfulness by which he outlines complex ideas/concepts and provides clear examples throughout the book. We both were also pleased to see the attention and space given to how to use a variety of visual tools to present and explain data to policymakers. At the same time, we agreed that the book is not an introductory text and is probably most appropriate for practiced quantitative researchers and policy analysts who have a firm, conceptual knowledge of linear regression and non-experimental design, as well as experience with Stata syntax. Most of the book content focuses on the how to, and often does not include a detailed explanation for why a researcher might make specific analytical decisions. For example, although Chapter 6 provides an overview of descriptive tests like measures of central tendency, little space is given to when and how researchers should run basic inferential tests to identify significant differences between groups. As such, some readers may find it useful to pair this book with more foundational quantitative text(s) that have content focused on how and when to use inferential tests and different forms of regression. However, in all, we feel that Higher Education Policy Analysis Using Quantitative Techniques: Data Methods and Presentations is a solid addition to Springers Quantitative Methods in the Humanities and Social Sciences. Tituss text, full of detailed, step-by-step explanations of policy-specific statistical tests, data tools, and example Stata code, will make a great resource for academic researchers and policy analysts.
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