Essential Statistics for Public Managers and Policy Analysts / Edition 1

Essential Statistics for Public Managers and Policy Analysts / Edition 1

by Evan M. Berman
     
 

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 assumptions, regression, and path analysis. All but the most basic of

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Overview

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 assumptions, regression, and path analysis. All but the most basic of the calculations presented in the text are intended to be performed using statistics software. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Product Details

ISBN-13:
9781568026473
Publisher:
Congressional Quarterly, Inc.
Publication date:
09/01/2001
Edition description:
Older Edition
Pages:
195

Table of Contents

Prefacexv
Statistics Roadmapxx
Introductionxxiii
Chapter 1Why Research? An Introduction1
Chapter Objectives1
Research Design3
Six Steps3
Relationships5
Rival Hypotheses and Limitations of Experimental Study Designs8
Measurement and Sampling11
Measuring Concepts11
Measuring Variables: Levels and Scales12
Measuring Variables: Sampling16
Data Collection18
Administrative Data18
Surveys19
Other Data Sources20
Putting It Together21
Conclusion23
Key Terms24
Notes24
Chapter 2Univariate Analysis: Description27
Chapter Objectives27
Measures of Central Tendency29
The Mean29
The Median30
The Mode32
Using Grouped Data33
Measures of Dispersion35
Boxplots35
Frequency Distributions38
Standard Deviation40
Conclusion45
Key Terms45
Notes46
Chapter 3Hypothesis Testing With Chi-Square49
Chapter Objectives49
Contingency Tables50
Chi-Square51
Hypothesis Testing53
The Null Hypothesis55
Statistical Significance56
The Five Steps of Hypothesis Testing57
Chi-Square Test Assumptions59
Statistical Significance and Sample Size60
A Useful Digression: The Goodness-of-Fit Test62
The Practical Significance of Relationships64
Rival Hypotheses: Adding a Control Variable66
Conclusion67
Key Terms69
Notes69
Chapter 4Measures of Association71
Chapter Objectives71
Proportional Reduction in Error72
Calculating PRE72
Paired Cases73
Statistics for Two Nominal Variables74
Two Nominal Variables75
The Problem of Dependent Samples77
Small Sample Tests for Two-By-Two Tables78
Statistics for Mixed Ordinal-Nominal Data80
Evaluating Rankings80
Equivalency of Two Samples83
Statistics for Two Ordinal Variables84
Conclusion87
Key Terms88
Notes88
Chapter 5T-Tests and Anova93
Chapter Objectives93
Creating Index Variables94
T-Tests96
T-Test Assumptions98
A Working Example101
Analysis of Variance104
ANOVA Assumptions108
A Working Example108
Conclusion111
Key Terms112
Notes113
Chapter 6Regression I: Estimation117
Chapter Objectives117
Simple Regression118
Scatterplot119
Test of Significance119
Goodness of Fit121
Assumptions and Notation123
Multiple Regression124
Model Specification124
A Working Example126
Goodness of Fit for Multiple Regression128
Standardized Coefficients128
F-Test129
Use of Nominal Variables129
Conclusion131
Key Terms132
Notes132
Chapter 7Regression II: Assumptions, Time Series135
Chapter Objectives135
Testing Assumptions136
Outliers136
Multicollinearity137
Linearity139
Heteroscedasticity140
Measurement and Specification142
Time Series Analysis145
Detecting Autocorrelation145
Correcting Autocorrelation146
Policy Evaluation148
Lagged Variables150
Forecasting151
Forecasting with Few Observations152
Forecasting with Periodic Effects155
Conclusion157
Key Terms157
Notes158
Chapter 8Advanced Statistics159
Chapter Objectives159
Logistic Regression160
Path Analysis163
Survival Analysis166
Regression-Based Forecasting167
Forecasting with Leading Indicators168
Curve Estimation168
Exponential Smoothing169
ARIMA171
Precis of Other Techniques171
Beyond Logistic Regression172
Exploratory Analysis172
Beyond Life Tables173
Beyond One-Way ANOVA173
Beyond Path Analysis174
Conclusion174
Key Terms175
Notes175
AppendixStatistical Tables177
Normal Distribution178
Chi-square Distribution179
T-test Distribution180
F-test Distribution181
Durbin-Watson Distribution185

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