More New and Used

from Private Sellers

**Note:**Supplemental materials are not guaranteed with Rental or Used book purchases.

# Essential Statistics for Public Managers and Policy Analysts

ISBN13:

## 9781608716777

**by**Berman, Evan M., Ph.D.; Wang, XiaoHu, Ph.D.

1608716775

3rd

Paperback

11/17/2011

Cq Pr

### Questions About This Book?

### Why should I rent this book?

Renting is easy, fast, and cheap! Renting from eCampus.com can save you hundreds of dollars compared to the cost of new or used books each semester. At the end of the semester, simply ship the book back to us with a free UPS shipping label! No need to worry about selling it back.

### How do rental returns work?

Returning books is as easy as possible. As your rental due date approaches, we will email you several courtesy reminders. When you are ready to return, you can print a free UPS shipping label from our website at any time. Then, just return the book to your UPS driver or any staffed UPS location. You can even use the same box we shipped it in!

### What version or edition is this?

This is the 3rd edition with a publication date of 11/17/2011.

### What is included with this book?

- The
**New**copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc. - The
**Used**copy of this book is not guaranteed to include any supplemental materials. Typically, only the book itself is included. - The
**Rental**copy of this book is not guaranteed to include any supplemental materials. You may receive a brand new copy, but typically, only the book itself. - The
**eBook**copy of this book is not guaranteed to include any supplemental materials. Typically only the book itself is included.

### Summary

Essential Statistics: Known for its brevity and student-friendly approach, this new third edition of Essential Statistics provides students with a strong conceptual foundation, but continues to stress application. Class-tested learning objectives, key term lists, and numeroustables, figures, and charts further enhance skill acquisition. Fully updated, this edition touts: two new chapters on applications in performance management and analysis and ANOVA new coverage of essential nonparametric alternatives to conventional inferential statistics additional material on performance management, going beyond an emphasis on performance measurement.

### Table of Contents

Tables, Figures, and Boxes | p. xii |

Preface | p. xvi |

Statistics Roadmap | p. xx |

Introduction | p. 1 |

Why Statistics for Public Managers and Analysts? | p. 4 |

Chapter Objectives | p. 4 |

Role of Data in Public Management | p. 4 |

Competency and Proficiency | p. 6 |

Ethics in Data Analysis and Research | p. 10 |

Summary | p. 14 |

Key Terms | p. 15 |

Research Methods | p. 17 |

Research Design | p. 21 |

Chapter Objectives | p. 21 |

Introducing Variables and Their Relationships | p. 21 |

Program Evaluation | p. 26 |

Six Steps | p. 28 |

Rival Hypotheses and Limitations of Experimental Study Designs | p. 30 |

Quasi-Experimental Designs in Program Evaluation | p. 32 |

Summary | p. 39 |

Key Terms | p. 40 |

Conceptualization and Measurement | p. 43 |

Chapter Objectives | p. 43 |

Measurement Levels and Scales | p. 43 |

Conceptualization | p. 48 |

Operationalization | p. 50 |

Index Variables | p. 53 |

Measurement Validity | p. 55 |

Summary | p. 57 |

Key Terms | p. 58 |

Measuring and Managing Performance: Present and Future | p. 61 |

Chapter Objectives | p. 61 |

Performance Measurement | p. 62 |

The Logic Model | p. 63 |

Further Examples | p. 67 |

Efficiency, Effectiveness, and a Bit More | p. 69 |

Managing Performance | p. 73 |

Peering into the Future: Forecasting | p. 75 |

Summary | p. 78 |

Key Terms | p. 78 |

Data Collection | p. 80 |

Chapter Objectives | p. 80 |

Sources of Data | p. 81 |

Administrative Data | p. 81 |

Secondary Data | p. 83 |

Surveys | p. 85 |

Other Sources | p. 89 |

Sampling | p. 90 |

When Is a Sample Needed? | p. 90 |

How Should Samples Be Selected? | p. 91 |

How Large Should the Sample Be? | p. 92 |

Data Input | p. 94 |

Putting It Together | p. 98 |

Summary | p. 100 |

Key Terms | p. 101 |

Descriptive Statistics | p. 103 |

Central Tendency | p. 105 |

Chapter Objectives | p. 105 |

The Mean | p. 106 |

The Median | p. 109 |

The Mode | p. 112 |

Summary | p. 112 |

Key Terms | p. 113 |

Appendix 6.1: Using Grouped Data | p. 113 |

Measures of Dispersion | p. 117 |

Chapter Objectives | p. 117 |

Frequency Distributions | p. 118 |

Standard Deviation | p. 122 |

Definition | p. 122 |

Some Applications of Standard Deviation | p. 124 |

Summary | p. 128 |

Key Terms | p. 129 |

Appendix 7.1: Boxplots | p. 129 |

Contingency Tables | p. 134 |

Chapter Objectives | p. 134 |

Contingency Tables | p. 135 |

Relationship and Direction | p. 138 |

Pivot Tables | p. 141 |

Summary | p. 144 |

Key Terms | p. 146 |

Getting Results | p. 148 |

Chapter Objectives | p. 148 |

Analysis of Outputs and Outcomes | p. 149 |

Analysis of Efficiency and Effectiveness | p. 152 |

Analysis of Equity | p. 154 |

Quality-of-Life Analysis | p. 155 |

Some Cautions in Analysis and Presentation | p. 157 |

The Use of Multiple Measures | p. 157 |

Treatment of Missing Values | p. 159 |

Summary | p. 161 |

Key Terms | p. 162 |

Inferential Statistics | p. 163 |

Hypothesis Testing with Chi-Square | p. 166 |

Chapter Objectives | p. 166 |

What Is Chi-Square? | p. 167 |

Hypothesis Testing | p. 169 |

The Null Hypothesis | p. 170 |

Statistical Significance | p. 171 |

The Five Steps of Hypothesis Testing | p. 174 |

Chi-Square Test Assumptions | p. 177 |

Statistical Significance and Sample Size | p. 178 |

A Nonparametric Alternative | p. 181 |

Summary | p. 182 |

Key Terms | p. 183 |

Appendix 10.1: Rival Hypotheses: Adding a Control Variable | p. 183 |

Measures of Association | p. 188 |

Chapter Objectives | p. 188 |

Three New Concepts | p. 189 |

PRE: The Strength of Relationships | p. 189 |

Paired Cases: The Direction of Relationships | p. 190 |

Dependent Samples | p. 191 |

PRE Alternatives to Chi-Square | p. 192 |

Beating the Standard? The Goodness-of-Fit Test | p. 195 |

Discrimination and Other Tests | p. 197 |

Do the Evaluators Agree? | p. 199 |

Summary | p. 201 |

Key Terms | p. 202 |

The T-Test | p. 205 |

Chapter Objectives | p. 205 |

T-Tests for Independent Samples | p. 206 |

T-Test Assumptions | p. 208 |

Working Example 1 | p. 212 |

Working Example 2 | p. 214 |

Two T-Test Variations | p. 217 |

Paired-Samples T-Test | p. 217 |

One-Sample T-Test | p. 218 |

Nonparametric Alternatives to T-Tests | p. 219 |

Summary | p. 221 |

Key Terms | p. 222 |

Analysis of Variance (ANOVA) | p. 226 |

Chapter Objectives | p. 226 |

Analysis of Variance | p. 227 |

ANOVA Assumptions | p. 229 |

A Working Example | p. 230 |

Beyond One-Way ANOVA | p. 234 |

A Nonparametric Alternative | p. 235 |

Summary | p. 236 |

Key Terms | p. 237 |

Simple Regression | p. 239 |

Chapter Objectives | p. 239 |

Simple Regression | p. 240 |

Scatterplot | p. 240 |

Test of Significance | p. 241 |

Assumptions and Notation | p. 244 |

Pearson's Correlation Coefficient | p. 245 |

Spearman's Rank Correlation Coefficient | p. 256 |

Summary | p. 249 |

Key Terms | p. 249 |

Multiple Regression | p. 252 |

Chapter Objectives | p. 252 |

Model Specification | p. 253 |

A Working Example | p. 256 |

Further Statistics | p. 259 |

Goodness of Fit for Multiple Regression | p. 259 |

Standardized Coefficients | p. 259 |

F-Test | p. 260 |

Use of Nominal variables | p. 261 |

Testing Assumptions | p. 263 |

Outliers | p. 263 |

Multicollinearity | p. 265 |

Linearity | p. 266 |

Heteroscedasticity | p. 267 |

Autocorrelation | p. 268 |

Measurement and Specification | p. 270 |

Summary | p. 273 |

Key Terms | p. 273 |

Further Statistics | p. 277 |

Logistic Regression | p. 279 |

Chapter Objectives | p. 279 |

The Logistic Model | p. 280 |

A Working Example | p. 281 |

Calculating Event Probabilities | p. 283 |

Summary | p. 286 |

Key Terms | p. 286 |

Time Series Analysis | p. 287 |

Chapter Objectives | p. 287 |

Time Series Data in Multiple Regression | p. 288 |

Autocorrelation | p. 288 |

Correcting Autocorrelation | p. 290 |

Policy Evaluation | p. 291 |

Lagged Variables | p. 293 |

Statistical Forecasting Methods: A Primer | p. 294 |

Regression-Based Forecasting | p. 297 |

Forecasting with Leading Indicators | p. 298 |

Curve Estimation | p. 298 |

Exponential Smoothing | p. 299 |

ARIMA | p. 300 |

Non-Regression-Based Forecasting with Few Observations | p. 301 |

Forecasting with Periodic Effects | p. 304 |

Summary | p. 306 |

Key Terms | p. 306 |

Survey of Other Techniques | p. 309 |

Chapter Objectives | p. 309 |

Path Analysis | p. 309 |

Beyond Path Analysis | p. 313 |

Survival Analysis | p. 314 |

Beyond Life Tables | p. 315 |

Factor Analysis | p. 316 |

Beyond Factor Analysis | p. 317 |

Summary | p. 320 |

Key Terms | p. 320 |

Appendix: Statistical Tables | p. 323 |

Normal Distribution | p. 324 |

Chi-Square Distribution | p. 325 |

T-Test Distribution | p. 326 |

Durbin-Watson Distribution | p. 328 |

F-Test Distribution | p. 329 |

Glossary | p. 333 |

Index | p. 349 |

Table of Contents provided by Ingram. All Rights Reserved. |