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Overview
As the first book ever published for public administration statistics courses, APPLIED STATISTICS FOR PUBLIC AND NONPROFIT ADMINISTRATION makes a difficult subject accessible to students and practitioners of public administration and to non-profit studies who have little background in statistics or research methods. Steeped in experience and practice, this landmark text remains the first and best in research methods and statistics for students and practitioners in public-and nonprofit-administration. All statistical techniques used by public administration professionals are covered, and all examples in the text relate to public administration and the nonprofit sector. Avoiding jargon and formula, this text uses a step-by-step approach that facilitates student learning.
- In response to reviewer feedback, the chapters have been reworked for the ninth edition: -Chapter 7 (Introduction to Probability) and Chapter 22 (Interrupted Time Series: Program and Policy Analysis) from the eighth edition are now integrated into surrounding chapters, thus improving the flow of the textbook as well as trimming some of the overall length. -Part 7 from the eighth edition (Special Topics in Quantitative Management) has been removed entirely in order to keep the focus of the textbook on the most important information for its audience.
- The ninth edition incorporates more discussion of problems and examples that are important to the nonprofit sector and organizations. The text and examples are now even more interesting to students with a background in nonprofit organizations or aspirations to work in the nonprofit sector.
- Upon recommendation from reviewers, faculty, and students, more Excel®-based problems, identified in the text with a computer icon, have been included in this edition. Data sets for these problems are available for instructors and students on the text's companion website.
- As in previous editions, answers to odd-numbered computational problems are provided at the back of the book. In addition, you can access answers to all problems in the Instructor's Manual.
- Increased coverage of research designs.
- Accessibility: The text provides students of different backgrounds the tools needed to learn and apply statistical methods quickly and easily. No previous background or preparation in statistics, methods, or analysis is needed.
- Practicality: This textbook addresses practical problems and presents examples that practitioners in the field of public and nonprofit administration face.
- Usefulness: The authors give students the tools and examples they need to analyze and make sense of data, to interpret the strengths and limitations of the results obtained, and to communicate their findings clearly and persuasively.
- Problem Solving: This text includes problem sets at the end of each chapter to reinforce student learning.
- Computer Application: The authors include data sets in Excel® available to both the instructor and students. These data sets from public and nonprofit administration provide the opportunity to use statistical package programs such as SPSS®, SAS®, STATA®, and Minitab® to analyze data and obtain results on the computer.
1. Statistics and Public and Nonprofit Administration.
2. Measurement.
3. Research Design.
PART II: DESCRIPTIVE STATISTICS.
4. Frequency Distributions.
5. Measures of Central Tendency.
6. Measures of Dispersion.
PART III: PROBABILITY.
7. The Normal Probability Distribution.
8. The Binomial Probability Distribution.
9. Some Special Probability Distributions.
PART IV: INFERENTIAL STATISTICS.
10. Introduction to Inference.
11. Hypothesis Testing.
12. Estimating Population Proportions.
13. Testing the Difference between Two Groups.
PART V: ANALYSIS OF NOMINAL AND ORDINAL DATA.
14. Construction and Analysis of Contingency Tables.
15. Aids for the Interpretation of Contingency Tables.
16. Statistical Control Table Analysis.
PART VI: REGRESSION ANALYSIS.
17. Introduction to Regression Analysis.
18. The Assumptions of Linear Regression.
19. Time Series Analysis.
20. Multiple Regression.
21. Regression Output and Data Management.