Higher Education Policy Analysis Using Quantitative Techniques: Data, Methods and Presentation (Quantitative Methods in the Humanities and Social Sciences)

✍ Scribed by Marvin Titus

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 249
• Category: Library

✦ Synopsis

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).

✦ Table of Contents

Acknowledgments
Contents
About the Author
1 Introduction
References
2 Asking the Right Policy Questions
2.1 Introduction
2.2 Asking the Right Policy Questions
2.2.1 The What Questions
2.2.2 The How Questions
2.2.3 The How Questions and Quantitative Techniques
2.2.4 So Many Answers and Not Enough Time
2.2.5 Answers in Search of Questions
2.3 Summary
References
3 Identifying Data Sources
3.1 Introduction
3.2 International Data
3.3 National Data
3.4 State-Level Data
3.5 Institution-Level Data
3.6 Summary
References
4 Creating Datasets and Managing Data
4.1 Introduction
4.2 Stata Dataset Creation
4.2.1 Primary Data
4.2.2 Secondary Data
4.3 Summary
4.4 Appendix
References
5 Getting to Know Thy Data
5.1 Introduction
5.2 Getting to Know the Structure of Our Datasets
5.3 Getting to Know Our Data
5.4 Missing Data Analysis
5.4.1 Missing Data—Missing Completely at Random
5.5 Summary
5.6 Appendix
References
6 Using Descriptive Statistics and Graphs
6.1 Introduction
6.2 Descriptive Statistics
6.2.1 Measures of Central Tendency
6.2.2 Measures of Dispersion
6.2.3 Distributions
6.3 Graphs
6.3.1 Graphs—EDA
6.4 Conclusion
6.5 Appendix
Reference
7 Introduction to Intermediate Statistical Techniques
7.1 Introduction
7.2 Review of OLS Regression
7.2.1 The Assumptions of OLS Regression
7.2.2 Bivariate OLS Regression
7.2.3 Multivariate OLS Regression
7.2.4 Multivariate Pooled OLS Regression
7.2.4.1 Multivariate Pooled OLS Regression with Interaction Terms
7.3 Weighted Least Squares and Feasible Generalized Least Squares Regression
7.4 Fixed-Effects Regression
7.4.1 Unobserved Heterogeneity and Fixed-Effects Dummy Variable (FEDV) Regression
7.4.2 Estimating FEDV Multivariate POLS Regression Models
7.4.2.1 Unobserved Heterogeneity and Within-Group Estimator Fixed-Effects Regression
7.4.2.2 Limitations of Fixed-Effects Regression Models
7.4.3 Fixed-Effects Regression and Difference-in-Differences
7.4.3.1 The DiD Estimator
7.4.3.2 Fixed-Effects Regression-Based DiD: An Example
7.4.3.3 DiD Placebo Tests
7.5 Random-Effects Regression
7.5.1 Hausman Test
7.6 Summary
7.7 Appendix
References
8 Advanced Statistical Techniques: I
8.1 Introduction
8.2 Time Series Data and Autocorrelation
8.3 Testing for Autocorrelations
8.3.1 Examples of Autocorrelation Tests—Time Series Data
8.4 Time Series Regression Models with AR terms
8.4.1 Autocorrelation of the Residuals from the P-W Regression
8.5 Summary of Time Series Data, Autocorrelation, and Regression
8.6 Examples of Autocorrelation Tests—Panel Data
8.7 Panel-Data Regression Models with AR Terms
8.8 Cross-Sectional Dependence
8.8.1 Cross-Sectional Dependence—Unobserved Common Factors
8.8.2 Tests to Detect Cross-Sectional Dependence—Unobserved Common Factors
8.9 Panel Regression Models That Take Cross-Sectional Dependency into Account
8.10 Summary
8.11 Appendix
References
9 Advanced Statistical Techniques: II
9.1 Introduction
9.2 The Context of Macro Panel Data and an Appropriate Statistical Approach
9.2.1 Heterogeneous Coefficient Regression
9.2.2 Macro Panel Data
9.2.3 Common Correlated Effects Estimators
9.2.4 HCR with a DCCE Estimator
9.2.5 Error Correction Model Framework
9.2.6 Mean Group Estimator
9.2.6.1 Short-Run and Long-Run Coefficients
9.3 Demonstration of HCR with DCCE and MG Estimators
9.3.1 Macroeconomic Panel Data
9.3.2 Tests for Nonstationary Data
9.3.3 Tests for Cointegration
9.3.4 Tests for Cross-Sectional Independence
9.3.5 Test of Homogeneous Coefficients
9.3.6 Results of the HCR with DCCE and MG Estimators
9.4 Summary
9.5 Appendix
References
10 Presenting Analyses to Policymakers
10.1 Introduction
10.2 Presenting Descriptive Statistics
10.2.1 Descriptive Statistics in Microsoft Word Tables
10.3 Choropleth Maps
10.4 Graphs
10.4.1 Graphs of Regression Results
10.5 Marginal Effects (with Continuous Variables) and Graphs
10.5.1 Marginal Effects (Elasticities) and Graphs
10.6 Marginal Effects and Word Tables
10.7 Marginal Effects (with Categorical Variables) and Graphs
10.8 Summary
10.9 Appendix
References
Index