Methods for Quantitative Macro-Comparative Research - Babones, Salvatore J.

Résumé :
Will a one-child policy increase economic growth? Does globalization contribute to global warming? 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. 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.This should be required reading for World Bank, OECD and U.N. researchers and data collectors as well as applied and academic sociologists, economists, political scientists and others who conduct cross country comparisons using publicly available large datasets. -Ernesto Casta?eda, University of Texas at El Paso I really don't know how the author has managed it, but he covers complex material in an incredibly clear way…I think students who have a weaker background in statistics will learn a lot from the text and students with an advanced background in statistics will look at their analyses in a different way (from the point of planning analyses to actually interpreting results). -Lesley Williams Reid, Georgia State University

Biographie:
Salvatore J. Babones is a senior lecturer in sociology and social policy at the University of Sydney and an associate fellow at the Institute for Policy Studies (IPS). Previously, he was an assistant professor of sociology, public health, and public and international affairs at the University of Pittsburgh. He holds both a PhD in sociology and an MSE in mathematical sciences from the Johns Hopkins University. Dr. Babones is the author or editor of eight books and more than thirty academic papers. He is the editor of Applied Statistical Modeling and Fundamentals of Regression Modeling, both published by SAGE as part of the Benchmarks in Social Research Methods reference series. His academic research focuses on globalization, economic development, and statistical methods for comparative social science research.

Sommaire:
PART I: MACRO-COMPARATIVE DATA STRUCTURES
1. The Logic of Macro-Comparative Research
2. The International Data Infrastructure
3. Variable Operationalization
4. The Structure of Country Data
PART II: STATISTICAL ANALYSIS OF MACRO-COMPARATIVE DATA
5. Statistical Modeling with Cross-Sectional Designs
6. Structured and Longitudinal Designs for Establishing Causality
7. Repeated Measures and Multilevel Modeling
8. An Interpretive Research and Policy Framework
Conclusion: The Political Economy of Quantitative Macro-Comparative Research

Critique:

This is a book of PRIZE WINNING qualityâ¿?It is also a profoundly original, highly convincing and very innovative attempt to reformulate macro methods to avoid the pitfalls of the clich?d solutions that have filled macro journals with sloppy unconvincing pseudo-scientific analyses for the last twenty years.

-Samuel Cohn, Texas A&M University