This Fourth Edition continues to offer a conceptual understanding of statistics that can be applied readily to the real-life challenges of public administrators and policy analysts. The book provides examples from the areas of human resources management, organizational behaviour, budgeting, and public policy to illustrate how public administrators interact with and analyse data.
Evan M. Berman is Professor of Public Management and Director of Internationalization at Victoria University of Wellington, School of Government. Prior, he was the Huey McElveen Distinguished Professor at Louisiana State University. His areas of expertise are human resource management, public performance, local government, and public governance in Asia. He is past Chair of the American Society for Public Administration's Section of Personnel and Labor Relations. He has over 125 publications and 12 books, including People Skills At Work (CRC Press, 2011), Essential Statistics for Public Managers and Policy Analysts, Third Edition (CQ Press, 2012), and a trilogy of books on Public Administration in Asia (2010, 2011, 2013, CRC Press). He has published in all major journals of the discipline, is Senior Editor of Public Performance & Management Review, a Distinguished Fulbright Scholar, past University Chair Professor at National Chengchi University (Taipei, Taiwan), and a former policy analyst with the National Science Foundation.
Tables, Figures, and Boxes Preface Acknowledgments Statistics Roadmap Section I: Introduction Chapter 1 Why Statistics for Public Managers and Policy Analysts? Chapter Objectives Role of Data in Public Management Competency and Proficiency Ethics in Data Analysis and Research Summary Key Terms Section II: Research Methods Chapter 2 Research Design Chapter Objectives Introducing Variables and Their Relationships Program Evaluation A Bit More: Extending through Quasi-experimental Design Summary Key Terms Chapter 3 Conceptualization and Measurement Chapter Objectives Measurement Levels and Scales Conceptualization Operationalization Index Variables Measurement Validity Summary Key Terms Chapter 4 Measuring and Managing Performance: Present and Future Chapter Objectives Performance Measurement Managing Performance Efficiency, Effectiveness, and a Bit More Peering Into the Future: Forecasting Summary Key Terms Chapter 5 Data Collection Chapter Objectives Sources of Data Sampling Data Input Putting It Together Summary Key Terms Section III: Descriptive Statistics Chapter 6 Central Tendency Chapter Objectives The Mean The Median The Mode Summary Key Terms Appendix 6.1: Using Grouped Data Chapter 7 Measures of Dispersion Chapter Objectives Frequency Distributions Standard Deviation Summary Key Terms Appendix 7.1: Boxplots Chapter 8 Contingency Tables Chapter Objectives Contingency Tables Relationship and Direction Pivot Tables Summary Key Terms Chapter 9 Getting Results Chapter Objectives Analysis of Outputs and Outcomes Analysis of Efficiency and Effectiveness Analysis of Equity Quality-of-Life Analysis A Bit of Forecasting, Too Some Cautions in Analysis and Presentation Summary Key Terms Appendix 9.1: Forecasting with Periodic Effects Section IV: Inferential Statistics Chapter 10 Introducing Inference: Estimation from Samples Chapter Objectives From Sample to Population Statistical Estimation of Population Parameters Summary Key Terms Chapter 11 Hypothesis Testing with Chi-Square Chapter Objectives What Is Chi-Square? Hypothesis Testing The Goodness-of-Fit Test A Nonparametric Alternative Summary Key Terms Appendix 11.1: Rival Hypotheses: Adding a Control Variable Appendix 11.2: Some Nonparametric Tests for Specific Situations Chapter 12 The T-Test Chapter Objectives T-Tests for Independent Samples Two T-Test Variations Nonparametric Alternatives to T-Tests Summary Key Terms Chapter 13 Analysis of Variance (ANOVA) Chapter Objectives Analysis of Variance A Nonparametric Alternative Summary Key Terms Chapter 14 Simple Regression Chapter Objectives Simple Regression Pearson's Correlation Coefficient Spearman's Rank Correlation Coefficient Summary Key Terms Chapter 15 Multiple Regression Chapter Objectives Model Specification A Working Example Further Statistics Use of Nominal Variables Testing Assumptions Summary Key Terms Section V: Further Statistics Chapter 16 Logistic and Time Series Regression Chapter Objectives The Logistic Model A Working Example Time Series in Multiple Regression Summary Key Terms Chapter 17 Survey of Other Techniques Chapter Objectives Path Analysis Statistical Forecasting Survival Analysis Factor Analysis Summary Key Terms Appendixes A: Normal Distribution B: Chi-Square (c2) Distribution C: T-Test Distribution D: Durbin-Watson Distribution E: F-Test Distribution Glossary Index About the Authors
