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 Castañeda, 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
