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Summary
Summary
Will a one-child policy increase economic growth? Does globalization contribute to global warming? Are unequal societies less healthy than more egalitarian societies? It is questions like these that social scientists turn to quantitative macro-comparative research (QMCR) to answer. Although many social scientists understand statistics conceptually, they struggle with the mathematical skills required to conduct QMCR. This non-mathematical book is intended to bridge that gap, interpreting the advanced statistics used in QMCR in terms of verbal descriptions that any college graduate with a basic background in statistics can follow. It addresses both the philosophical foundations and day-to-day practice of QMCR in an effort to improve research outcomes and ensure policy relevance. A comprehensive guide to QMCR, the book presents an overview of the questions that can be answered using QMCR, details the steps of the research process, and concludes with important guidelines and best practices for conducting QMCR. The book assumes that the reader has a sound grasp of the fundamentals of linear regression modeling, but no advanced mathematical knowledge is required in order for researchers and students to read, understand, and enjoy the book. A conversational discussion style supplemented by 75 tables and figures makes the book's methodological arguments accessible to both students and professionals. Extensive citations refer readers back to primary discussions in the literature, and a comprehensive index provides easy access to coverage of specific techniques.
Table of Contents
Preface | p. xi |
Introduction | p. xv |
Macro-Comparative Data Structures | p. xviii |
Statistical Analysis of Macro-Comparative Data | p. xxi |
About the Author | p. xxvii |
Part I Macro-Comparative Data Structures | p. 1 |
1 The Logic of Quantitative Macro-Comparative Research | p. 3 |
Society as a Complex System | p. 6 |
The Micro-Macro Link | p. 1 |
The Complexity Science Approach | p. 8 |
The Two Systems | p. 10 |
Levels and Units of Analysis | p. 11 |
The Country as Unit | p. 12 |
Data-Generating Processes | p. 15 |
Gallon and the One-world Problem | p. 19 |
Implications of Compositional Interdependence | p. 20 |
Positive Versus Interpretive Analysis | p. 23 |
2 The International Data Infrastructure | p. 27 |
Sources of Broadly Cross-National Data | p. 30 |
The World Development Indicators | p. 30 |
Other Official Sources | p. 33 |
NGO and Specialist Sources | p. 36 |
Sources of Detailed Rich-Country Data | p. 39 |
Data on Individuals | p. 41 |
Standardized Social Surveys | p. 41 |
Survey and Census Repositories | p. 45 |
Emerging Forms of Data | p. 46 |
Internet Metadata | p. 47 |
Satellite Imagery | p. 49 |
Systematic Qualitative Data | p. 49 |
3 Variable Operationalization | p. 51 |
Transforming Variables | p. 53 |
Standardization | p. 54 |
Normalization | p. 56 |
Operationalizing National Income | p. 59 |
National Income Concepts | p. 61 |
Currency Conversion Factors | p. 62 |
Reference Year | p. 65 |
Correlational Characteristics of National Income Operationalizations | p. 66 |
Challenges in Operationalizing Other Economic Variables | p. 66 |
Inequality and Poverty | p. 68 |
Trade, Investment, and Globalization | p. 70 |
Concentration, Penetration, and Dependency | p. 71 |
Operationalization Challenges Relating to Noneconomic Variables | p. 72 |
4 Cross-National Data Structures and Their Properties | p. 77 |
Database Construction With Country Data | p. 80 |
Balanced and Unbalanced Panels | p. 83 |
Sparse Data and the Treatment of Missing Cases | p. 88 |
Patterns in the Available Data | p. 91 |
A Data Cross-Section of the World Today | p. 94 |
The Africa-Europe Axis | p. 96 |
Spatial Dependence | p. 98 |
The Time Characteristics of Country Data | p. 99 |
Autocorrelations and Lag Structures | p. 100 |
Time Points and the Width of a Time Point | p. 102 |
Part II Statistical Analysis of Macro-Comparative Data | p. 105 |
5 Statistical Modeling With Cross-Sectional Designs | p. 107 |
The Statistical Modeling of Entire Data Populations | p. 108 |
Error Sampling and the Significance Testing Controversy | p. 110 |
Measurement Error and Regression Attenuation | p. 113 |
Nonrandom Assignment in Data Populations | p. 116 |
Building and Specifying Cross-Sectional Models | p. 118 |
Model Building | p. 121 |
National Income as a Contextualizing Control Variable | p. 123 |
Competing and Complementary Controls | p. 126 |
Triangulation Using Multiple Models | p. 129 |
6 Structured and Longitudinal Designs for Establishing Causality | p. 133 |
Conditions for Causality | p. 135 |
Structural Equation Modeling as a Template for Causality | p. 136 |
The Three Principles of Causality | p. 137 |
Model Designs for Establishing Con-elation, Precedence, and Nonspuriousness | p. 139 |
Establishing Precedence Using Instrumental Variables | p. 145 |
Two-Stage Least Squares Regression Models | p. 147 |
Structural Equation Models With Reciprocal Effects | p. 149 |
Establishing Nonspuriousness Using Longitudinal Models | p. 153 |
Long-Term Lagged Dependent Variable Models | p. 154 |
Difference Models | p. 157 |
7 Repeated Measures and Multilevel Modeling | p. 161 |
The Structure of Repeated Measures Data | p. 164 |
The Problem of Nonspherical Errors | p. 164 |
Correcting for Mean Dependence | p. 166 |
Correcting for Variance Dependence | p. 169 |
Time Series Cross-Sectional Models | p. 170 |
Multilevel Models | p. 174 |
The Fixed Effects Model | p. 175 |
The Random Effects Model | p. 177 |
Making Appropriate Use of Repeated Measures Data | p. 180 |
Time-Invariant Independent Variables | p. 181 |
Lags and Trends | p. 183 |
The Slope-Slope Model | p. 185 |
8 An Interpretive Research and Policy Framework | p. 187 |
Empirical Research as Data Description | p. 190 |
Multiple Models as Multiple Lenses | p. 191 |
Viewing Results as Contextually Valid | p. 195 |
The Role of Theory in the Search for Causality | p. 198 |
From p-Values to Grounded Theory | p. 202 |
Causality, Endogeneity, and Meaning | p. 205 |
The Policy Implications of Research Findings | p. 208 |
Conclusion | p. 213 |
9 The Political Economy of Quantitative Macro-Comparative Research | p. 215 |
Industrial Imperatives for Research "Productivity" | p. 216 |
The Challenge From Computational "Social" Science | p. 219 |
Five Basic Rules for the Quantitative Analysis of Macro-Comparative Data | p. 221 |
References | p. 225 |
Author Index | p. 241 |
Subject Index | p. 247 |