Seven Rules for Social Research teaches social scientists how to get the most out of their technical skills and tools, providing a resource that fully describes the strategies and concepts no researcher or student of human behavior can do without.
Glenn Firebaugh provides indispensable practical guidance for anyone doing research in the social and health sciences today, whether they are undergraduate or graduate students embarking on their first major research projects or seasoned professionals seeking to incorporate new methods into their research. The rules are the basis for discussions of a broad range of issues, from choosing a research question to inferring causal relationships, and are illustrated with applications and case studies from sociology, economics, political science, and related fields. Though geared toward quantitative methods, the rules also work for qualitative research.
Seven Rules for Social Research is ideal for students and researchers who want to take their technical skills to new levels of precision and insight, and for instructors who want a textbook for a second methods course.
The Seven Rules
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Preface xi
Chapter 1: The First Rule
There Should Be the Possibility of Surprise in Social Research 1
Selecting a Research Question 2
Researchable Questions 2
Interesting Questions 4
Selecting a Sample 18
Samples in Qualitative Studies 23
Is Meaningful Social Research Possible? 26
Summary 29
Student Exercises on Rule 1 31
Chapter 2: The Second Rule
Look for Differences That Make a Difference, and Report Them 36
You Can't Explain a Variable with a Constant 37
Maximizing Variance to Find the Effect of a Cause 39
Size versus Statistical Significance 41
Comparing Effects Where There Is a Common Metric 42
Calibration: Converting Explanatory Variables to a Common Metric 44
Substantive Profiling: The Use of Telling Comparisons 46
Visual Presentation of Results 51
Policy Importance 53
Importance for Theory 54
Conclusion 56
Student Exercises on Rule 2 58
Chapter 3: The Third Rule
Build Reality Checks into Your Research 64
Internal Reality Checks 65
Reality Checks on Data--Dubious Values and Incomplete Data 65
Reality Checks on Measures--Aim for Consistency in Conceptualization and Measurement 69
Reality Checks on Models--The Formal Equivalence Check 71
External Reality Checks: Validation with Other Data and Methods 76
Using Causal-Process Observations to Test Plausibility of Results 77
Using Ethnographic Data to Help Interpret Survey Results 79
Other Examples of Multiple-Method Research 81
Concluding Remark 82
Student Exercises on Rule 3 84
Chapter 4: The Fourth Rule
Replicate Where Possible 90
Sources of Uncertainty in Social Research 91
Overview: From Population to Sample and Back to Population 93
Measurement Error as a Source of Uncertainty 100
Illustration: Two Methods for Estimating Global Poverty 101
Toward a Solution: Identical Analyses of Parallel Data Sets 105
Meta-analysis: Synthesizing Results Formally across Studies 106
Summary: Your Confidence Intervals Are Too Narrow 109
Student Exercises on Rule 4 111
Chapter 5: The Fifth Rule
Compare Like with Like 120
Correlation and Causality 121
Types of Strategies for Comparing Like with Like 129
Matching versus Looking for Differences 130
The Standard Regression Method for Comparing Like with Like 131
Critique of the Standard Linear Regression Strategy 132
Comparing Like with Like Through Fixed-Effects Methods 134
First-Difference Models: Subtracting Out the Effects of Confounding Variables 134
Special Case: Growth-Rate Models 138
Sibling Models 140
Comparing Like with Like through Matching on Measured Variables 146
Exact Matching 146
Propensity-Score Method 147
Matching as a Preprocessing Strategy for Reducing Model Dependence 151
Comparing Like with Like through Naturally Occurring Random Assignment 152
Instrumental Variables: Matching through Partial Random Assignment 153
Matching Through Naturally Occurring Random Assignment to the Treatment Group 158
Comparison of Strategies for Comparing Like with Like 159
Conclusion 162
Student Exercises on Rule 5 165
Chapter 6: The Sixth Rule
Use Panel Data to Study Individual Change and Repeated Cross-section Data to Study Social Change 172
Analytic Differences between Panel and Repeated Cross-section Data 173
Three General Questions about Change 175
Changing-Effect Models, Part 1: Two Points in Time 176
Changing-Effect Models, Part 2: Multilevel Models with Time as the Context 182
What We Want to Know 183
The General Multilevel Model 183
Convergence Models 185
The Sign Test for Convergence: Comparing Your fs and ds 186
Convergence Model versus Changing-Effect Model 191
Bridging Individual and Social Change: Estimating Cohort Replacement Effects 195
An Accounting Scheme for Social Change 197
Linear Decomposition Method 198
Summary 201
Student Exercises on Rule 6 203
Chapter 7: The Seventh Rule
Let Method Be the Servant, Not the Master 207
Obsession with Regression 209
Naturally Occurring Random Assignment, Again 209
Decomposition Work in the Social Sciences 218
Decomposition of Variance and Inequality 220
Decomposition of Segregation Indexes 222
The Effects of Social Context 226
Context Effects as Objects of Study 227
Context Effects as Nuisance 230
Critical Tests in Social Research 231
Conclusion 235
Student Exercises on Rule 7 236
References 241
Index 253
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