Efnisyfirlit

• Tables, Figures, and Features
• Preface
• About the Authors
• Chapter 1 • Introduction
• Research on Income Inequality
• Politics and the Gender Gap
• The Case of Italian (Non) Tax Compliance
• Protests and Repression in New Democracies
• The Observer Effect in International Politics: Evidence from a Natural Experiment
• Conclusion
• Terms Introduced
• Chapter 2 • The Empirical Approach to Political Science
• Elements of Empiricism
• The Importance of Theory
• An Example: Proximity Theory of Voting
• The Explanatory Range of Theories
• A Brief Overview of the Empirical Research Process
• Development of an Idea to Investigate or a Problem to Solve
• Hypothesis Formation
• “Data” Collection
• Interpretation and Decision
• Modification and Extension
• Reactions to the Empirical Approach: Practical Objections
• Self-Reflection and Individuality
• Is Political Science Trivial or Irrelevant?
• Competing Points of View
• Interpretation
• Constructionism and Critical Theory
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 3 • Beginning the Research Process: Identifying a Research Topic, Developing Research Questions, and Reviewing the Literature
• Specifying the Research Question
• Sources of Ideas for Research Topics
• Why Conduct a Literature Review?
• Collecting Sources for a Literature Review
• Identifying the Relevant Scholarly Literature
• Managing Citations
• Identifying Useful Popular Sources
• Reading the Literature
• Writing a Literature Review
• Anatomy of a Literature Review
• Scientific Relevance
• Building a Theory
• Disagreement in the Literature
• Data and Methods
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 4 • The Building Blocks of Social Scientific Research: Hypotheses, Concepts, Variables, and Measurement
• Proposing Explanations
• Variables
• Formulating Hypotheses
• Characteristics of Good Hypotheses
• Specifying Units of Analysis
• Cross-Level Analysis: Ecological Inference and Ecological Fallacy
• Defining Concepts
• Devising Measurement Strategies
• The Accuracy of Measurements
• Reliability
• Validity
• The Precision of Measurements
• Levels of Measurement
• Working with Precision: Too Little or Too Much
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 5 • Sampling
• The Basics of Sampling
• How Do We Use a Sample to Learn about a Population?
• Sampling Distribution
• Sample Size and Margin of Error
• Sampling Methods
• Types of Samples
• Simple Random Samples
• Systematic Random Samples
• Stratified Samples
• Cluster Samples
• Nonprobability Samples
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 6 • Research Design: Establishing Causation
• Verifying Causal Assertions
• Causal versus Spurious Relationships
• The Classical Randomized Experiment
• Internal Validity
• External Validity
• Qualitative and Quantitative Methods and Analysis: Causes-of-Effects and Effects-of-Causes Approaches
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 7 • Qualitative Research: Case Study Designs
• Case Study Methods
• Case Study Types
• Purposes of Case Studies
• The Logic of Case Selection and Case Comparison
• Using Cases to Explore Causal Mechanisms: Process Tracing
• Generalizing from Case Studies
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 8 • Making Empirical Observations: Qualitative Analysis
• Types of Data and Collection Techniques
• Choosing among Data Collection Methods
• Data Collection in Qualitative Research
• Observation
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 9 • Quantitative Research Designs
• Randomized Experimental Designs
• Posttest Design
• Repeated-Measurement Design
• Multiple-Group Design
• Randomized Field Experiments
• Natural Experiments
• Nonrandomized Designs: Quasi-Experiments
• Observational Studies
• Cross-Sectional Design
• Longitudinal (Time Series) Designs
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 10 • Quantitative Methods
• The Wide Variety of Quantitative Projects
• Sources of Data for Quantitative Studies
• Content Analysis
• Content Analysis Procedures
• Surveys
• Questionnaire Design Issues
• Question Wording
• Question Order
• Survey Types
• Data Management
• Finding and Downloading Data
• Recording Data
• Managing Data
• Ethical Concerns with Quantitative Methods
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 11 • Making Sense of Data: First Steps
• The Data Matrix
• Data Description and Exploration
• Frequency Distributions, Proportions, and Percentages
• Descriptive Statistics
• Measures of Central Tendency
• Resistant Measures
• Measures of Dispersion
• Graphs for Presentation and Exploration
• Designing and Drawing Graphs
• Bar Charts
• Exploratory Graphs
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 12 • Testing Relationships
• The Normal Distribution and z Scores
• Confidence Intervals
• Population Confidence Intervals
• Sample Confidence Intervals
• Hypothesis Testing
• Types of Hypotheses
• Steps for Hypothesis Testing
• Difference of Means Tests
• Testing Hypotheses about Proportions
• Reinforcing Interpretation of Hypothesis Testing
• Testing a Relationship with Two Samples
• Difference of Means with Related Samples
• Difference of Means with Independent Samples
• Confidence Interval with Two Samples
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 13 • Analyzing Relationships for Categorical Data
• The Basics of Identifying and Measuring Relationships
• Level of Measurement
• Types of Relationships
• Table Summaries of Categorical Variable Associations
• Measuring Strength of Relationships in Tables
• Direction of a Relationship
• Measures of Association: Statistics for Reporting the Strength of Relationships in Tables
• A Coefficient for Nominal Data
• Coefficients for Ordinal Variables
• Chi-Square Test for Independence
• Multivariate Analysis of Categorical Data
• Analysis of Variance: Analyzing the Difference between Means for More Than Two Means
• Explained and Unexplained Variation
• Significance Test for Analysis of Variance
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 14 • Regression
• Logic of Regression
• The Classical Assumptions of Linear Regression Models
• Scatterplots
• Minimizing the Sum of the Squared Error
• The Linear Regression Model
• Regression
• Measuring Correlation: Pearson’s r
• Measuring the Fit of a Regression Line: R-Squared
• Multivariate Regression
• Interpreting Regression Tables
• Categorical Independent Variables
• Maximum Likelihood Models for Dichotomous Dependent Variables
• The Logic of Maximum Likelihood
• Interpreting a Logistic Regression Table
• Predicted Probabilities
• Conclusion
• Terms Introduced
• Suggested Readings
• Chapter 15 • The Research Report: An Annotated Example
• Annotated Research Report Example
• Appendixes
• Appendix A: Normal Curve Tail Probabilities
• Appendix B: Critical Values from t Distribution
• Appendix C: Chi-Squared Distribution Values for Various Right-Tail Probabilities
• Appendix D: F Distribution
• Glossary
• Index