Essential Statistics for Public Managers and Policy Analysts 4th Edition by Evan Berman, XiaoHu Wang ISBN 1506364314 9781506364315
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ISBN 10: 1506364314
ISBN 13: 9781506364315
Author: Evan Berman, XiaoHu Wang
Known for its brevity and student-friendly approach, Essential Statistics for Public Managers and Policy Analysts remains one of the most popular introductory books on statistics for public policy and public administration students, using carefully selected examples tailored specifically for them. The 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 behavior, budgeting, and public policy to illustrate how public administrators interact with and analyze data.
Essential Statistics for Public Managers and Policy Analysts 4th Table of contents:
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
Six Steps
Rival Hypotheses and Limitations of Experimental Study Designs
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
The Logic Model
Further Examples
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
Administrative Data
Secondary Data
Surveys
Other Sources
Sampling
When Is a Sample Needed?
How Should Samples Be Selected?
How Large Should the Sample Be?
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
Definition
Some Applications of 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
The Use of Multiple Measures
Treatment of Missing Values
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
Large-Sample Confidence Intervals for a Mean
Small-Sample Confidence Intervals for a Mean
Confidence Intervals for a Proportion (Percentage)
Summary
Key Terms
Chapter 11 Hypothesis Testing with Chi-Square
Chapter Objectives
What Is Chi-Square?
Hypothesis Testing
The Null Hypothesis
Statistical Significance
The Five Steps of Hypothesis Testing
Chi-Square Test Assumptions
Statistical Significance and Sample Size
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
T-Test Assumptions
Working Example 1
Working Example 2
Two T-Test Variations
Paired-Samples T-Test
One-Sample T-Test
Nonparametric Alternatives to T-Tests
Summary
Key Terms
Chapter 13 Analysis of Variance (ANOVA)
Chapter Objectives
Analysis of Variance
ANOVA Assumptions
A Working Example
Beyond One-Way ANOVA
A Nonparametric Alternative
Summary
Key Terms
Chapter 14 Simple Regression
Chapter Objectives
Simple Regression
Scatterplot
Test of Significance
Assumptions and Notation
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
Goodness of Fit for Multiple Regression
Standardized Coefficients
F-Test
Use of Nominal Variables
Testing Assumptions
Outliers
Multicollinearity
Linearity
Heteroscedasticity
Autocorrelation
Measurement and Specification
Summary
Key Terms
Section V: Further Statistics
Chapter 16 Logistic and Time Series Regression
Chapter Objectives
The Logistic Model
A Working Example
Calculating Event Probabilities
Time Series in Multiple Regression
Autocorrelation
Correcting Autocorrelation
Policy Evaluation
Lagged Variables
Forecasting with Leading Indicators
Summary
Key Terms
Chapter 17 Survey of Other Techniques
Chapter Objectives
Path Analysis
Beyond Path Analysis
Statistical Forecasting
Curve Estimation
Exponential Smoothing
ARIMA
Survival Analysis
Beyond Life Tables
Factor Analysis
Beyond Factor Analysis
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