Table of Contents:
1. The Logic of Comparison and Analysis
• Introduction: Why Analyze Data?
• Some Statistical History
• Use of Statistics
• Theory Construction at a Glance
 What is Theory?
 Theory and Research
• The Process of Scientific Inquiry
 Observation and Inquisitiveness
 Primary Questions
 Research Questions
• Research: Movement from Theory to Data and Back
 Formulating Hypotheses
 Constructing the Research Design
 Conceptualization
 Operationalization
 Gathering the Data
• Statistical Analysis: The Art of Making Comparisons
 Foundations of Valid Comparisons
 Comparing Appropriate Phenomena
 Using Comparable Measures
 Choosing Analysis Methods that Best Summarize the Data
 Drawing Conclusions
 Communicating the Results
• Data and the Purposes of This Book
• Chapter Resources
2. Variables and Measurement
• The Variable Defined
• Transforming Characteristics Into Data: The Process of Measurement
• How Variables Can Differ
 Levels of Measurement
 Scale Continuity
 Use in the Research Process
• Conclusions
• Chapter Resources
3. Understanding Data Through Organization: Summarizing
Data in Tables, Frequency Distributions, and Graphical
Representation
• Frequency Distributions: A Chart of a Different Color
 Conventions for Building Distributions
 Frequency Distribution
 Percentage Distributions
 Combination Distributions
• Graphical Representations of Frequencies
 Pie Charts
 Histograms and Bar Charts
 Polygons and Area Charts
• Analyzing Univariate Statistics
• Analyzing Change
 Line Charts
 Ogives
• Analyzing Bivariate and Mulitvariate Data
 Scatter Plots
 Normal Probability Plots
 Path Diagrams
• Analyzing Geographic Distributions
 Pin, Spot, or Point Maps
 Choropleth Maps
• Conclusion
• Chapter Resources
4. Measures of Central Tendency
• Univariate Descriptive Statistics
• Measures of Central Tendency
 Mode
 Median
 Mean
• Selecting the Most Appropriate Measure of Central Tendency
• Conclusion
• Chapter Resources
5. Measures of Dispersion
• Deviation of Dispersion
• Measures of Dispersion
 Range
 Index of Dispersion
 Mean Absolute Deviation
 Variance
 Standard Deviation
 Uses for the Variance and Standard Deviation
• Selecting the Most Appropriate Measure of Dispersion
• Conclusion
• Chapter Resources
6. The Form of Distribution
• Moments of a Distribution
• Number of Modes
• Skewness
 Analysis of Skew
• Kurtosis
 Analysis of Kurtosis
• The Importance of Skew and Kurtosis
• Design of the Normal Curve
 Points to Remember About the Normal Curve
• Conclusion
• Chapter Resources
7. Introduction to Bivariate Descriptive Statistics
• Bivariate Tables and Analysis
 Statistical Tables versus Presentation Tables
• Constructing Bivariate Tables
 Ordinal Level Table Construction
 Nominal Level Table Construction
• Analysis of Bivariate Tables
• Conclusion
• Chapter resources
8. Measures of Existence and Statistical Significance
• Nominal Level Measures of Existence
• Tables, Percentages, and Differences
• ChiSquare
 Requirements of Using ChiSquare
 Limitations of ChiSquare
 Final Note on ChiSquare
• Tests of Existence for Ordinal and Interval Level Data
 Calculation and Interpretation for Ordinal Data
 Spearman's Rho and Pearson's r
• An Issue of Significance
• Conclusion
• Chapter resources
9. Measures of Strength of a Relationship
• What Is Association?
• Nominal Level Data
• Ordinal Level Data
 Tau
 Gamma
 Somers' d
 Spearman's Rho
• Interval Level Data
• Pearson's r
• Conclusion: Selecting the Most Appropriate Measure of Strength
• Chapter Resources

10. Measures of Direction and Nature of a Relationship
• Direction of the Association
 Establishing Direction for Ordinal Level Data
 Establishing Direction for Interval and Ratio Level Data
• Nature of Association
 Establishing the Nature of the Distribution for Nominal Level Data
 Nature of Association
 Establishing the Nature of the Distribution for Interval and Ratio Level
Data
• Conclusion
• Chapter resources
11. Introduction to Multivariate Statistics
• When Two Variables Just Aren't Enough
• Interaction Among Variables
• Causation
 Association
 Temporal Ordering
 Elimination of Confounding Variables
• Additional Concepts in Multivariate Analysis
 Robustness
 Error
 Parsimony
• Conclusion
• Chapter resources
12. Multiple Regression I: Ordinary Least Squares Regression
• Regression
• Assumptions
• Analysis and Interpretation
 Steps in OLS Regression Analysis
 Other OLS Regression Analysis
 Limitations of OLS Regression
• Multicollinearity
 Assessing Multicollinearity
 Adjusting for Multicollinearity
• Conclusion
• Chapter resources
13. Multiple Regression II: Limited Dependent Variables
• Dealing with Limited Dependent Variables
 OLS Assumptions That Are Violated by Dichotomous Variables
• Assumptions and Use of Logistic Regression
 Interpreting Logit Results
 Interactive Effects and Other Types of Logit
 Criticisms of Logistic Regression
• Poisson and Negative Binomial Regression
 Interpreting Poisson and Negative Binomial Regression
 Criticisms of Poisson and Negative Binomial Models
• Other Regression Procedures
 Probit Regression
 Tobit Regression
• Conclusion
• Chapter Resources
14. Introduction to Inferential Analysis
• Terminology and Assumptions
• Normal Curve
• Probability
• Sampling
 Probability Sampling
 Nonprobability Sampling
• Sampling Distributions
• Central Limit Theorem
• Confidence Levels
 Calculating Confidence Intervals
 Interpreting Confidence Intervals
• Conclusion
• Chapter Resources
15. Hypothesis Testing
• Null and Research Hypotheses
• Steps in Hypothesis Testing
• Type I and Type II Errors
 Which is Better, Type I or Type II Error?
• Power of Tests
• Conclusion
• Chapter Resources
16. Hypothesis Tests
• ZTest
 Calculation and Example
 Interpretation and Application: Known Probability of Error
 One versus TwoSample Ztests
• ttest
 Assumptions of a ttest
 Calculation and Example
 SPSS Analysis for Ztests and ttests
• Chisquare Test for Independence
• Conclusion
• Chapter Resources
17. Analysis of Variance (ANOVA)
• ANOVA
 Assumptions
 Calculation and Interpretation
• Post Hoc Tests
• Conclusion
• Chapter Resources
18. Putting It All Together
• The Relationship Between Statistics, Methodology, and Theory
• Describe It or Make Inferences
• Abuse of Statistics
• When You Are On Your Own
• Conclusion
• Chapter Resources
Index 