Normal view MARC view ISBD view

Quantitative social science : an introduction / Kosuke Imai.

By: Imai, Kosuke [author.].
Copyright date: Princeton, New Jersey, USA : Princeton University Press, ©2017Description: xix, 408 pages, 8 unnumbered pages of plates : illustrations (some color), maps (some color) ; 26 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780691175461 ; 0691175462; 9780691167039; 0691167036.Subject(s): Social sciences -- Methodology | Social sciences -- ResearchDDC classification: 300.72 Im17 2017
Contents:
Preface -- 1. Introduction: Overview of the Book ; How to Use this Book ; Introduction to R ; Summary ; Exercises -- 2. Causality: Racial Discrimination in the Labor Market ; Subsetting the Data in R ; Causal Effects and the Counterfactual ; Randomized Controlled Trials ; Observational Studies ; Descriptive Statistics for a Single Variable ; Summary ; Exercises -- 3. Measurement: Measuring Civilian Victimization during Wartime ; Handling Missing Data in R ; Visualizing the Univariate Distribution ; Survey Sampling ; Measuring Political Polarization ; Summarizing Bivariate Relationships ; Clustering ; Summary ; Exercises -- 4. Prediction: Predicting Election Outcomes ; Linear Regression ; Regression and causation ; Summary ; Exercises -- 5. Discovery: Textual Data ; Network Data ; Spatial Data ; Summary ; Exercises -- 6. Probability: Probability ; Conditional Probability ; Random Variables and Probability Distributions ; Large Sample Theorems ; Summary ; Exercises -- 7. Uncertainty: Estimation ; Hypothesis Testing ; Linear Regression Model with Uncertainty ; Summary ; Exercises -- 8. Next.
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. -- Provided by publisher.
Item type Current location Collection Call number Status Date due Barcode
Books Books College Library
General Circulation Section
GC GC 300.72 Im17 2017 (Browse shelf) Available HNU000208

Includes bibliographical references and indexes.

Preface -- 1. Introduction: Overview of the Book ; How to Use this Book ; Introduction to R ; Summary ; Exercises -- 2. Causality: Racial Discrimination in the Labor Market ; Subsetting the Data in R ; Causal Effects and the Counterfactual ; Randomized Controlled Trials ; Observational Studies ; Descriptive Statistics for a Single Variable ; Summary ; Exercises -- 3. Measurement: Measuring Civilian Victimization during Wartime ; Handling Missing Data in R ; Visualizing the Univariate Distribution ; Survey Sampling ; Measuring Political Polarization ; Summarizing Bivariate Relationships ; Clustering ; Summary ; Exercises -- 4. Prediction: Predicting Election Outcomes ; Linear Regression ; Regression and causation ; Summary ; Exercises -- 5. Discovery: Textual Data ; Network Data ; Spatial Data ; Summary ; Exercises -- 6. Probability: Probability ; Conditional Probability ; Random Variables and Probability Distributions ; Large Sample Theorems ; Summary ; Exercises -- 7. Uncertainty: Estimation ; Hypothesis Testing ; Linear Regression Model with Uncertainty ; Summary ; Exercises -- 8. 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. -- Provided by publisher.

College of Education Bachelor of Secondary Education major in Social Studies

Text in English

There are no comments for this item.

Log in to your account to post a comment.