| 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
• Chi-Square
- Requirements of Using Chi-Square
- Limitations of Chi-Square
- Final Note on Chi-Square
• 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
• Z-Test
- Calculation and Example
- Interpretation and Application: Known Probability of Error
- One- versus Two-Sample Z-tests
• t-test
- Assumptions of a t-test
- Calculation and Example
- SPSS Analysis for Z-tests and t-tests
• Chi-square 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 |
