Methods for quantitative macro-comparative research / Salvatore J. Babones, University of Sydney, Australia.

Format:

Book

Author/Creator:

Babones, Salvatore J., author.

Language:

English

Subjects (All):

Social sciences--Statistical methods.

Social sciences.

Social sciences--Methodology.

Social sciences--Research--Mathematical models.

Econometrics.

Social sciences--Research.

Physical Description:

1 online resource (267 pages) : illustrations

Place of Publication:

Thousand Oaks, California : SAGE, [2014]

System Details:

text file

Summary:

A critical guide to research design and statistical methods for quantitative macro-comparative research, Will a one-child policy increase economic growth? Does globalization contribute to climate change? Are unequal societies less healthy than more egalitarian societies? To answer questions like these, social scientists turn to quantitative macro-comparative research (QMCR). Although many social scientists understand statistics conceptually, they struggle with the mathematical skills required to conduct QMCR. In Methods for Quantitative Macro-Comparative Research, author Salvatore J. Babones offers a means 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. He addresses both the philosophical foundations and day-to-day practice of QMCR in an effort to improve research outcomes and ensure policy relevance. Book jacket.

Contents:

Part I Macro-Comparative Data Structures 1

1 The Logic of Quantitative Macro-Comparative Research 3

Society as a Complex System 6

The Micro-Macro Link 1

The Complexity Science Approach 8

The Two Systems 10

Levels and Units of Analysis 11

The Country as Unit 12

Data-Generating Processes 15

Gallon and the One-world Problem 19

Implications of Compositional Interdependence 20

Positive Versus Interpretive Analysis 23

2 The International Data Infrastructure 27

Sources of Broadly Cross-National Data 30

The World Development Indicators 30

Other Official Sources 33

NGO and Specialist Sources 36

Sources of Detailed Rich-Country Data 39

Data on Individuals 41

Standardized Social Surveys 41

Survey and Census Repositories 45

Emerging Forms of Data 46

Internet Metadata 47

Satellite Imagery 49

Systematic Qualitative Data 49

3 Variable Operationalization 51

Transforming Variables 53

Standardization 54

Normalization 56

Operationalizing National Income 59

National Income Concepts 61

Currency Conversion Factors 62

Reference Year 65

Correlational Characteristics of National Income Operationalizations 66

Challenges in Operationalizing Other Economic Variables 66

Inequality and Poverty 68

Trade, Investment, and Globalization 70

Concentration, Penetration, and Dependency 71

Operationalization Challenges Relating to Noneconomic Variables 72

4 Cross-National Data Structures and Their Properties 77

Database Construction With Country Data 80

Balanced and Unbalanced Panels 83

Sparse Data and the Treatment of Missing Cases 88

Patterns in the Available Data 91

A Data Cross-Section of the World Today 94

The Africa-Europe Axis 96

Spatial Dependence 98

The Time Characteristics of Country Data 99

Autocorrelations and Lag Structures 100

Time Points and the Width of a Time Point 102

Part II Statistical Analysis of Macro-Comparative Data 105

5 Statistical Modeling With Cross-Sectional Designs 107

The Statistical Modeling of Entire Data Populations 108

Error Sampling and the Significance Testing Controversy 110

Measurement Error and Regression Attenuation 113

Nonrandom Assignment in Data Populations 116

Building and Specifying Cross-Sectional Models 118

Model Building 121

National Income as a Contextualizing Control Variable 123

Competing and Complementary Controls 126

Triangulation Using Multiple Models 129

6 Structured and Longitudinal Designs for Establishing Causality 133

Conditions for Causality 135

Structural Equation Modeling as a Template for Causality 136

The Three Principles of Causality 137

Model Designs for Establishing Con-elation, Precedence, and Nonspuriousness 139

Establishing Precedence Using Instrumental Variables 145

Two-Stage Least Squares Regression Models 147

Structural Equation Models With Reciprocal Effects 149

Establishing Nonspuriousness Using Longitudinal Models 153

Long-Term Lagged Dependent Variable Models 154

Difference Models 157

7 Repeated Measures and Multilevel Modeling 161

The Structure of Repeated Measures Data 164

The Problem of Nonspherical Errors 164

Correcting for Mean Dependence 166

Correcting for Variance Dependence 169

Time Series Cross-Sectional Models 170

Multilevel Models 174

The Fixed Effects Model 175

The Random Effects Model 177

Making Appropriate Use of Repeated Measures Data 180

Time-Invariant Independent Variables 181

Lags and Trends 183

The Slope-Slope Model 185

8 An Interpretive Research and Policy Framework 187

Empirical Research as Data Description 190

Multiple Models as Multiple Lenses 191

Viewing Results as Contextually Valid 195

The Role of Theory in the Search for Causality 198

From p-Values to Grounded Theory 202

Causality, Endogeneity, and Meaning 205

The Policy Implications of Research Findings 208

Conclusion 213

9 The Political Economy of Quantitative Macro-Comparative Research 215

Industrial Imperatives for Research "Productivity" 216

The Challenge From Computational "Social" Science 219

Five Basic Rules for the Quantitative Analysis of Macro-Comparative Data 221.

Notes:

Description based on MARC record for print version.

Other Format:

Print version :

ISBN:

9781544307749

OCLC:

1020006543

Access Restriction:

Restricted for use by site license.