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APPLIED SOCIAL SCIENCE METHODOLOGY. AN INTRODUCTORY GUIDE
Título:
APPLIED SOCIAL SCIENCE METHODOLOGY. AN INTRODUCTORY GUIDE
Subtítulo:
Autor:
GERRING, J
Editorial:
CAMBRIDGE UNIVERSITY PRESS
Año de edición:
2017
ISBN:
978-1-107-41681-9
Páginas:
442
56,68 €

 

Sinopsis

This textbook provides a clear, concise, and comprehensive introduction to methodological issues encountered by the various social science disciplines. It emphasizes applications, with detailed examples, so that readers can put these methods to work in their research. Within a unified framework, John Gerring and Dino Christenson integrate a variety of methods - descriptive and causal, observational and experimental, qualitative and quantitative. The text covers a wide range of topics including research design, data-gathering techniques, statistics, theoretical frameworks, and social science writing. It is designed both for those attempting to make sense of social science, as well as those aiming to conduct original research. The text is accompanied by online practice questions, exercises, examples, and additional resources, including related readings and websites. An essential resource for undergraduate and postgraduate programs in communications, criminal justice, economics, business, finance, management, education, environmental policy, international development, law, political science, public health, public policy, social work, sociology, and urban planning.

A clear, concise and comprehensive introduction to social science methodology, with an emphasis on application
Uses a unified framework, integrating a variety of methods including descriptive and causal, observational and experimental, and qualitative and quantitative
Supplementary online resources include questions, exercises, chapter lecture slides, a hypothetical data set and suggestions for further reading
Applicable to all social science disciplines including politics, sociology, psychology, anthropology, business, and economics



Table of Contents

Preface
Part I. Building Blocks:
1. A unified framework
2. Arguments
3. Concepts and measures
4. Analyses
Part II. Causality:
5. Causal frameworks
6. Causal hypotheses and analyses
7. Experimental designs
8. Large-N observational designs
9. Case study designs
10. Diverse tools of causal inference
Part III. Process and Presentation:
11. Reading and reviewing
12. Brainstorming
13. Data gathering
14. Writing
15. Speaking
16. Ethics
Part IV. Statistics:
17. Data management
18. Univariate statistics
19. Probability distributions
20. Statistical inference
21. Bivariate statistics
22. Regression
23. Causal inference
24. Appendix
References
Index.