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Higher Education Policy Analysis Using Quantitative Techniques: Data, Methods and Presentation 2021 ed. [Kõva köide]

  • Formaat: Hardback, 243 pages, kõrgus x laius: 235x155 mm, kaal: 553 g, 35 Illustrations, black and white; XI, 243 p. 35 illus., 1 Hardback
  • Sari: Quantitative Methods in the Humanities and Social Sciences
  • Ilmumisaeg: 15-May-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030608301
  • ISBN-13: 9783030608309
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Higher Education Policy Analysis Using Quantitative Techniques: Data, Methods and Presentation 2021 ed.Suurem pilt
  • Formaat: Hardback, 243 pages, kõrgus x laius: 235x155 mm, kaal: 553 g, 35 Illustrations, black and white; XI, 243 p. 35 illus., 1 Hardback
  • Sari: Quantitative Methods in the Humanities and Social Sciences
  • Ilmumisaeg: 15-May-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030608301
  • ISBN-13: 9783030608309
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030608309.html
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).

1 Introduction
1(8)
References
6(3)
2 Asking the Right Policy Questions
9(10)
2.1 Introduction
9(1)
2.2 Asking the Right Policy Questions
10(8)
2.2.1 The What Questions
11(3)
2.2.2 The How Questions
14(1)
2.2.3 The How Questions and Quantitative Techniques
15(2)
2.2.4 So Many Answers and Not Enough Time
17(1)
2.2.5 Answers in Search of Questions
17(1)
2.3 Summary
18(1)
References
18(1)
3 Identifying Data Sources
19(14)
3.1 Introduction
19(1)
3.2 International Data
20(1)
3.3 National Data
20(4)
3.4 State-Level Data
24(2)
3.5 Institution-Level Data
26(1)
3.6 Summary
27(1)
References
28(5)
4 Creating Datasets and Managing Data
33(20)
4.1 Introduction
33(1)
4.2 Stata Dataset Creation
34(14)
4.2.1 Primary Data
34(1)
4.2.2 Secondary Data
35(13)
4.3 Summary
48(1)
4.4 Appendix
49(2)
References
51(2)
5 Getting to Know Thy Data
53(26)
5.1 Introduction
53(1)
5.2 Getting to Know the Structure of Our Datasets
54(7)
5.3 Getting to Know Our Data
61(2)
5.4 Missing Data Analysis
63(11)
5.4.1 Missing Data---Missing Completely at Random
71(3)
5.5 Summary
74(1)
5.6 Appendix
74(3)
References
77(2)
6 Using Descriptive Statistics and Graphs
79(24)
6.1 Introduction
79(1)
6.2 Descriptive Statistics
80(12)
6.2.1 Measures of Central Tendency
80(5)
6.2.2 Measures of Dispersion
85(1)
6.2.3 Distributions
86(6)
6.3 Graphs
92(8)
6.3.1 Graphs---EDA
92(8)
6.4 Conclusion
100(1)
6.5 Appendix
100(2)
Reference
102(1)
7 Introduction to Intermediate Statistical Techniques
103(42)
7.1 Introduction
103(1)
7.2 Review of OLS Regression
104(17)
7.2.1 The Assumptions of OLS Regression
104(1)
7.2.2 Bivariate OLS Regression
105(3)
7.2.3 Multivariate OLS Regression
108(2)
7.2.4 Multivariate Pooled OLS Regression
110(11)
7.3 Weighted Least Squares and Feasible Generalized Least Squares Regression
121(1)
7.4 Fixed-Effects Regression
121(13)
7.4.1 Unobserved Heterogeneity and Fixed-Effects Dummy Variable (FEDV) Regression
122(1)
7.4.2 Estimating FEDV Multivariate POLS Regression Models
122(6)
7.4.3 Fixed-Effects Regression and Difference-in-Differences
128(6)
7.5 Random-Effects Regression
134(7)
7.5.1 Hausman Test
136(5)
7.6 Summary
141(1)
7.7 Appendix
141(3)
References
144(1)
8 Advanced Statistical Techniques: I
145(36)
8.1 Introduction
145(1)
8.2 Time Series Data and Autocorrelation
146(5)
8.3 Testing for Autocorrelations
151(2)
8.3.1 Examples of Autocorrelation Tests---Time Series Data
152(1)
8.4 Time Series Regression Models with AR terms
153(10)
8.4.1 Autocorrelation of the Residuals from the P-W Regression
154(9)
8.5 Summary of Time Series Data, Autocorrelation, and Regression
163(1)
8.6 Examples of Autocorrelation Tests---Panel Data
163(1)
8.7 Panel-Data Regression Models with AR Terms
164(3)
8.8 Cross-Sectional Dependence
167(6)
8.8.1 Cross-Sectional Dependence---Unobserved Common Factors
168(1)
8.8.2 Tests to Detect Cross-Sectional Dependence---Unobserved Common Factors
168(5)
8.9 Panel Regression Models That Take Cross-Sectional Dependency into Account
173(3)
8.10 Summary
176(1)
8.11 Appendix
176(3)
References
179(2)
9 Advanced Statistical Techniques: II
181(26)
9.1 Introduction
181(1)
9.2 The Context of Macro Panel Data and an Appropriate Statistical Approach
182(5)
9.2.1 Heterogeneous Coefficient Regression
182(1)
9.2.2 Macro Panel Data
183(1)
9.2.3 Common Correlated Effects Estimators
184(1)
9.2.4 HCR with a DCCE Estimator
185(1)
9.2.5 Error Correction Model Framework
186(1)
9.2.6 Mean Group Estimator
186(1)
9.3 Demonstration of HCR with DCCE and MG Estimators
187(14)
9.3.1 Macroeconomic Panel Data
188(1)
9.3.2 Tests for Nonstationary Data
188(6)
9.3.3 Tests for Cointegration
194(2)
9.3.4 Tests for Cross-Sectional Independence
196(1)
9.3.5 Test of Homogeneous Coefficients
197(1)
9.3.6 Results of the HCR with DCCE and MG Estimators
198(3)
9.4 Summary
201(1)
9.5 Appendix
202(1)
References
203(4)
10 Presenting Analyses to Policymakers
207(34)
10.1 Introduction
207(1)
10.2 Presenting Descriptive Statistics
208(4)
10.2.1 Descriptive Statistics in Microsoft Word Tables
208(4)
10.3 Choropleth Maps
212(2)
10.4 Graphs
214(7)
10.4.1 Graphs of Regression Results
216(5)
10.5 Marginal Effects (with Continuous Variables) and Graphs
221(8)
10.5.1 Marginal Effects (Elasticities) and Graphs
225(4)
10.6 Marginal Effects and Word Tables
229(2)
10.7 Marginal Effects (with Categorical Variables) and Graphs
231(1)
10.8 Summary
232(1)
10.9 Appendix
233(7)
References
240(1)
Index 241
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.