Quantitative social science: an introduction Imai, Kosuke

By:

Imai, Kosuke

Material type: Text

TextPublication details: Princeton Princeton University Press 2017Description: xix, 408 pISBN:

9780691175461

Subject(s):

DDC classification:

300.72 I6Q8

Summary: Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides. http://press.princeton.edu/titles/11025.html

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Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad	Non-fiction	300.72 I6Q8 (Browse shelf(Opens below))	Available		195226

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	300.72 I6Q8 Quantitative social science: an introduction	300.72 K2F2 Failing in the field: what we can learn when field research goes wrong	300.72 L2/2013 Landscape of qualitative research	300.72 L2A7 The art and science of portraiture	Next

Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science.

Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior.

Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors.

Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science
Provides hands-on instruction using R programming, not paper-and-pencil statistics
Includes more than forty data sets from actual research for students to test their skills on
Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools
Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises
Offers a solid foundation for further study
Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides.

http://press.princeton.edu/titles/11025.html

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