Methods for Quantitative Macro-Comparative Research
- Salvatore J. Babones - University of Sydney, Australia
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.
The following review regards the book “Methods for quantitative Macro-Comparative Research”, by Salvatore J. Babones.
The book has two big sections: i) “Macro-comparative data structures” which introduces several concepts regarding the logic of quantitative macro comparative research and the use/organization of international data structure; ii) statistical analysis of macro-comparative data. In particular this part introduces to statistical modeling with cross sectional and structured and longitudinal designs. In addition some chapters treat causality, repeated measures and multilevel modeling.
The book is quite discursive, formula/math seems to be avoided to let the reader focus more on concepts than on statistic. In the text there are several figures/schemes which facilitate the reader in the compression of several concepts however, those who benefit most from the book are those who already have the basics of statistic. In fact, readers who are interested in learning how to conduct the analysis, on the subjects described in the text, should refer to other books.
There are some suggestions the Author could consider for future improvements :
1) adding examples which can be conducted using statistical softwares (SPSS/Stata). The examples could guide the reader in experiencing, what it is said in the text. Moreover the reader could became more confident in conducting the analysis.
2) At the beginning of each chapter a brief introduction of what it is going to be found in the following sections could help the reader in familiarizing with the content of the chapter.
3) At the end of each chapter a brief summary could be usefull for reminding the most important concepts met (“what you have learned”).
4) Some exercises could be placed at the end of each chapter to allow the reader a practical revision of what was discussed.
Dr. Gabriele Messina
Research Professor of Public Health
University of Siena
Excellent book which will be a very useful supplement for our students evaluating comparative research
This is an excellent book on a complex topic, the author has made a fantastic job of explaining difficult statistical concept in a language that is easily acceptable to both students and researchers.
