Essential Statistics for Public Managers and Policy Analysts

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

• ISBN13: 9781608716777
• ISBN10: 1608716775
• Edition: 3rd
• Format: Paperback
• Copyright: 2011-11-17
• Publisher: Cq Pr

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
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