Cover image for Essential statistics for public managers and policy analysts
Title:
Essential statistics for public managers and policy analysts
Author:
Berman, Evan M.
Personal Author:
Publication Information:
Washington, D.C. : CQ Press, [2002]

©2002
Physical Description:
xxv, 195 pages : illustrations ; 23 cm
Language:
English
ISBN:
9781568026473
Format :
Book

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Library
Call Number
Material Type
Home Location
Status
Central Library HA29 .B425 2002 Adult Non-Fiction Central Closed Stacks
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Summary

Summary

In this text for students, public managers, and policy analysts, Berman (public administration, U. of Central Florida) illustrates the principles of statistics by applying them to familiar public issues. Topics include, for example, univariate analysis, hypothesis testing with chi-square, ANOVA assu


Author Notes

Evan M. Berman is associate professor in the Department of Public Administration at the University of Central Florida in Orlando.


Reviews 1

Choice Review

Berman (Univ. of Central Florida) in this brief book emphasizes the conceptual understanding of essential statistics using a very user friendly, noncumbersome calculation format and applying it to common public issues and problems. The author uses a straightforward approach by de-emphasizing what could be intimidating calculations to students. The text begins with "Statistics Road Map," which provides guidance to the reader for selecting an appropriate research tool. A wide array of statistical techniques offers useful resources for students and individuals in the areas of policy analysis and public management. Topics such as univariate analysis, hypothesis testing with chi-square analysis, regression, t-tests and ANOVA, logistic regression, and path analysis are briefly discussed. Recommended for lower- and upper-division undergraduates, professionals, and college libraries. D. J. Gougeon University of Scranton


Table of Contents

Prefacep. xv
Statistics Roadmapp. xx
Introductionp. xxiii
Chapter 1 Why Research? An Introductionp. 1
Chapter Objectivesp. 1
Research Designp. 3
Six Stepsp. 3
Relationshipsp. 5
Rival Hypotheses and Limitations of Experimental Study Designsp. 8
Measurement and Samplingp. 11
Measuring Conceptsp. 11
Measuring Variables: Levels and Scalesp. 12
Measuring Variables: Samplingp. 16
Data Collectionp. 18
Administrative Datap. 18
Surveysp. 19
Other Data Sourcesp. 20
Putting It Togetherp. 21
Conclusionp. 23
Key Termsp. 24
Notesp. 24
Chapter 2 Univariate Analysis: Descriptionp. 27
Chapter Objectivesp. 27
Measures of Central Tendencyp. 29
The Meanp. 29
The Medianp. 30
The Modep. 32
Using Grouped Datap. 33
Measures of Dispersionp. 35
Boxplotsp. 35
Frequency Distributionsp. 38
Standard Deviationp. 40
Conclusionp. 45
Key Termsp. 45
Notesp. 46
Chapter 3 Hypothesis Testing With Chi-Squarep. 49
Chapter Objectivesp. 49
Contingency Tablesp. 50
Chi-Squarep. 51
Hypothesis Testingp. 53
The Null Hypothesisp. 55
Statistical Significancep. 56
The Five Steps of Hypothesis Testingp. 57
Chi-Square Test Assumptionsp. 59
Statistical Significance and Sample Sizep. 60
A Useful Digression: The Goodness-of-Fit Testp. 62
The Practical Significance of Relationshipsp. 64
Rival Hypotheses: Adding a Control Variablep. 66
Conclusionp. 67
Key Termsp. 69
Notesp. 69
Chapter 4 Measures of Associationp. 71
Chapter Objectivesp. 71
Proportional Reduction in Errorp. 72
Calculating PREp. 72
Paired Casesp. 73
Statistics for Two Nominal Variablesp. 74
Two Nominal Variablesp. 75
The Problem of Dependent Samplesp. 77
Small Sample Tests for Two-By-Two Tablesp. 78
Statistics for Mixed Ordinal-Nominal Datap. 80
Evaluating Rankingsp. 80
Equivalency of Two Samplesp. 83
Statistics for Two Ordinal Variablesp. 84
Conclusionp. 87
Key Termsp. 88
Notesp. 88
Chapter 5 T-Tests and Anovap. 93
Chapter Objectivesp. 93
Creating Index Variablesp. 94
T-Testsp. 96
T-Test Assumptionsp. 98
A Working Examplep. 101
Analysis of Variancep. 104
ANOVA Assumptionsp. 108
A Working Examplep. 108
Conclusionp. 111
Key Termsp. 112
Notesp. 113
Chapter 6 Regression I: Estimationp. 117
Chapter Objectivesp. 117
Simple Regressionp. 118
Scatterplotp. 119
Test of Significancep. 119
Goodness of Fitp. 121
Assumptions and Notationp. 123
Multiple Regressionp. 124
Model Specificationp. 124
A Working Examplep. 126
Goodness of Fit for Multiple Regressionp. 128
Standardized Coefficientsp. 128
F-Testp. 129
Use of Nominal Variablesp. 129
Conclusionp. 131
Key Termsp. 132
Notesp. 132
Chapter 7 Regression II: Assumptions, Time Seriesp. 135
Chapter Objectivesp. 135
Testing Assumptionsp. 136
Outliersp. 136
Multicollinearityp. 137
Linearityp. 139
Heteroscedasticityp. 140
Measurement and Specificationp. 142
Time Series Analysisp. 145
Detecting Autocorrelationp. 145
Correcting Autocorrelationp. 146
Policy Evaluationp. 148
Lagged Variablesp. 150
Forecastingp. 151
Forecasting with Few Observationsp. 152
Forecasting with Periodic Effectsp. 155
Conclusionp. 157
Key Termsp. 157
Notesp. 158
Chapter 8 Advanced Statisticsp. 159
Chapter Objectivesp. 159
Logistic Regressionp. 160
Path Analysisp. 163
Survival Analysisp. 166
Regression-Based Forecastingp. 167
Forecasting with Leading Indicatorsp. 168
Curve Estimationp. 168
Exponential Smoothingp. 169
ARIMAp. 171
Precis of Other Techniquesp. 171
Beyond Logistic Regressionp. 172
Exploratory Analysisp. 172
Beyond Life Tablesp. 173
Beyond One-Way ANOVAp. 173
Beyond Path Analysisp. 174
Conclusionp. 174
Key Termsp. 175
Notesp. 175
Appendix Statistical Tablesp. 177
Normal Distributionp. 178
Chi-square Distributionp. 179
T-test Distributionp. 180
F-test Distributionp. 181
Durbin-Watson Distributionp. 185

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