List of Figures
List of Tables
Acknowledgments
1 The Scientific Study of Society
Overview
1.1 Social
Science?
1.2 Approaching Sociology Scientifically: The Search for Causal Explanations
1.3 Thinking about the World in Terms of Variables and Causal Explanations
1.4 Models of Society
1.5 Rules of the Road to Scientific Knowledge about Society
1.5.1 Make Your Theories Causal
1.5.2 Don't Let Data Alone Drive Your Theories
1.5.3 Consider Only Empirical Evidence
1.5.4 Avoid Normative Statements
1.5.5 Pursue Both Generality and Parsimony
1.6 The Ethics of Social Research
1.6.1 Potential Harm
1.6.2 Informed Consent
1.6.3 Deception
1.6.4 Anonymity and Confidentiality
1.7 A Quick Look Ahead
Concepts Introduced in This Chapter
Exercises
2 The Art of Theory Building
Overview
2.1 Good Theories Come from Good Theory-Building Strategies
2.2 Promising Theories Offer Answers to Interesting Research Questions
2.3 Identifying Interesting Variation
2.3.1 Time-Series Example
2.3.2 Cross-Sectional Examples
2.4 Learning to Use Your Knowledge
2.4.1 Moving from a Specific Event to More General Theories
2.4.2 Know Local, Think Global: Can You Drop the Proper Nouns?
2.5 Examine Previous Research
2.5.1 What Did the Previous Researchers Miss?
2.5.2 Can Their Theory Be Applied Elsewhere?
2.5.3 If We Believe Their Findings, Are There Further Implications?
2.5.4 How Might This Theory Work at Different Levels of Aggregation (Micro ↔ Macro?
2.6 How Do I Know if I Have a "Good" Theory?
2.6.1 Does Your Theory Offer an Answer to an Interesting Research Question?
2.6.2 Is Your Theory Causal?
2.6.3 Can You Test Your Theory on Data That You Have Not Yet Observed?
2.6.4 How General is Your Theory?
2.6.5 How Parsimonius is Your Theory?
2.6.6 How New Is Your Theory?
2.6.7 How Nonobvious is YourTheory?
2.7 Conclusion
Concepts Introduced in This Chapter
Exercises
3 Evaluating Causal Relationships
Overview
3.1 Causality and Everyday Language
3.2 Four Hurdles along the Route to Establishing Causal Relationships
3.2.1 Putting It All Together — Adding Up the Answers to Our Four Questions
3.2.2 Identifying Causal Claims Is an Essential Thinking Skill
3.2.3 What Are the Consequences of Failing to Control for Other Possible Causes?
3.3 Why Is Studying Causality So Important? Three Examples from Sociology
3.3.1 Intergroup Contact and Racial Tolerance
3.3.2 Race and Political Participation in the U.S.
3.3.3 Evaluating Whether Head Start Is Effective
3.4 Wrapping Up
Concepts Introduced in This Chapter
Exercises
4 Research Design
Overview
4.1 Comparison as the Key to Establishing Causal Relationships
4.2 Experimental Research Designs
4.2.1 "Random Assignment" versus "Random Sampling"
4.2.2 Varieties of Experiments and Near-Experiments
4.2.3 Are There Drawbacks to Experimental Research Designs?
4.3 Observational Studies (in Two Flavors)
4.3.1 Datum, Data, Data Set
4.3.2 Cross-Sectional Observational Studies
4.3.3 Time-Series Observational Studies
4.3.4 The Major Difficulty with Observational Studies
4.4 Summary
Concepts Introduced in This Chapter
Exercises
5 Survey Research
Overview
5.1 Why Surveys?
5.2 Modes of Survey Administration
5.2.1 Face-to-Face In-Person Interviews
5.2.2 Self-Administered Questionnaires
5.2.3 Telephone Interviews
5.2.4 Web-Based Surveys
5.2.5 Survey-Based Experiments
5.3 Already Existing Survey Data Sets
5.3.1 General Social Survey (GSS)
5.3.2 American National Election Study (ANES)
5.3.3 International Social Survey Progrmme (ISSP)
5.3.4 World Values Survey (WVS)
5.4 Probability Stampling
5.4.1 Simple Random Samples
5.4.2 Systematic Random Samples
5.4.3 Stratified Random Sampling
5.4.4 Multistage Cluster Sampling
Concepts Introduced in This Chapter
Exercises
6 Measuring Concepts of Interest
Overview
6.1 Getting to Know Your Data: Evaluating Measurement
6.2 Social Science Measurement: The Varying Challenges of Quantifying Humanity
6.3 Problems in Measuring Concepts of Interest
6.3.1 Conceptual Clarity
6.3.2 Reliability
6.3.3 Measurement Bias and Reliability
6.3.4 Validity
6.4.5 The Relationship between Validity and Reliability
6.4 Controversy: Measuring Racial Tolerance
6.5 Are There Consequences to Poor Measurement?
6.6 Conclusions
Concepts Introduced in This Chapter
Exercises
7 Getting to Know Your Data
Overview
7.1 Getting to Know Your Data Statistically
7.2 What is the Variable's Measurement Metric?
7.2.1 Categorical Variables
7.2.2 Ordinal Variables
7.2.3 Continuous Variables
7.2.4 Variable Types and Statistical Analyses
7.3 Describing Categorical Variables
7.4 Describing Continuous Variables
7.4.1 Rank Statistics
7.4.2 Moments
7.5 Limitations of Descriptive Statistics and Graphs
Concepts Introduced in This Chapter
Exercises
8 Probability and Statistical Inference
Overview
8.1 Populations and Samples
8.2 Some Basics of Probability Theory
8.3 Learning about the Population from a Sample: The Central Limit Theorem
8.3.1 The Normal Distribution
8.4 Example: Presidential Approval Ratings
8.4.1 What Kind of Sample Was That?
8.4.2 A Note on the Effects of Sample Size
8.5 A Look Ahead: Examining Relationships between Variables
Concepts Introduced in This Chapter
Exercises
9 Bivariate Hypothesis Testing
Overview
9.1 Bivariate Hypothesis Tests and Establishing Causal Relationships
9.2 Choosing the Right Bivariate Hypothesis Test
9.3 All Roads Lead to
p
9.3.1 The Logic of p-Values
9.3.2 The Limitations of p-Values
9.3.3 From p-Values to Statistical Significance
9.3.4 The Null Hypothesis and p-Values
9.4 Four Bivariate Hypothesis Tests
9.4.1 Example 1: Tabular Analysis
9.4.2 Example 2: Difference of Means
9.4.3 Example 3: Correlation Coefficient
9.4.4 Example 4: Analysis of Variance
9.5 Mutiple Comparisons
9.6 Wrapping Up
Concepts Introduced in This Chapter
Exercises
10 Two-Variable Regression Models
Overview
10.1 Two-Variable Regression
10.2 Fitting a Line: Population ↔ Sample
10.3 Which Line Fits Best? Estimating the Regression Line
10.4 Measuring Our Uncertainty about the OLS Regression Line
10.4.1 Goodness-of-Fit: Root Mean-Squared Error
10.4.2 Goodness-of-Fit: R-Squared Statistic
10.4.3 Is That a "Good" Goodness-of-Fit?
10.4.4 Uncertainty about Individual Component of the Sample Regression Model
10.4.5 Confidence Intervals about Parameter Estimates
10.4.6 Two-Tailed Hypothesis Tests
10.4.7 The Relationship between Confidence Intervals and Two-Tailed Hypothesis Tests
10.4.8 One-Tailed Hypothesis Tests
10.5 Assumptions, More Assumptions, and Minimal Mathematical Requirements
10.5.1 Assumptions about the Population Stochastic Component
10.5.2 Assumptions about Our Model Specification
10.5.3 Minimal Mathematical Requirements
10.5.4 How Can We Make All of These Assumptions?
Concepts Introduced in This Chapter
Exercises
11 Multiple Regression
Overview
11.1 Modeling Multivariate Reality
11.2 Adding a Z Variable to a Bivariate Tabular Analysis
11.3 The Population Regression Function
11.4 From Two-Variable to Multiple Regression
11.5 Interpreting Multiple Regression
11.6 Which Effect is "Biggest"?
11.7 Statistical and Substantive Significance
11.8 What Happens When We Fails to Control for Z
11.8.1 An Additional Minimal Mathematical Requirements in Multiple Regression
11.9 Being Smart with Dummy Independent Variables in OLS
11.9.1 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with Only Two Values
11.9.2 Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with More Than Two Values
11.9.3 Using Dummy Variables to Test Hypotheses about Multiple Independent Variables
11.10 Testing Interactive Hyotheses with Dummy Variables
11.11 Dummy Dependent Variables
11.11.1 The Linear Probability Model
11.11.2 Binomial Logit and Binomial Probit
11.11.3 Goodness-to-Fit with Dummy Dependent Variables
11.12 Implications
Concepts Introduced in This Chapter
Exercises
12 Putting It All Together to Produce Effective Research
Overview
12.1 Two Routes Toward a New Scientific Project
12.1.1 Project Type 1: A New Y (and Some X)
12.1.2 Project Type 2: An Existing Y and a New X
12.1.3 Variants on the Two Project Types
12.2 Using the Literature Without Getting Buried in it
12.2.1 Identifying the Important Work on a Subject — Using Citation Counts
12.2.2 Oh No! Someone Else Has Already Done What I Was Planning to Do. What Do I Do Now?
12.2.3 Dissecting the Research by Other Scholars
12.2.4 Read Effectively to Write Effectively
12.3 Writing Effectively about Your Research
12.3.1 Write Early, Write Often (Because Writing is Thinking)
12.3.2 Document Your Code — Writing and Thinking While You Compute
12.3.3 Divide and Conquer — a Section-by-Section Strategy for Building Your Project
12.3.4 Proofread, Proofread, and then Proofread Again
12.4 Making Effective Use of Tables and Figures
12.4.1 Constructing Regression Tables
12.4.2 Writing about Regression Tables
12.4.3 Other Types of Tables and Figures
Exercises
Appendix A. Critical Values of Chi-Squared
Appendix B. Critical Values of t
Appendix C. The Λ Link Function for Binomial Logit Models
Appendix D. The Φ Link Function for Binomial Probit Models
References
Index
