Big Data and Social Science (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Editionshows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. 
Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub 
New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter 
This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Big Data and Social Science: Data Science Methods and Tools for Research and Practice (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Foster, Ian

Kreuter, Frauke

Lane, Julia

Ghani, Rayid

Jarmin, Ron S.

Reference

Chapman and Hall/CRC

Product Description 
Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. 
Features:\nTakes an accessible, hands-on approach to handling new types of data in the social sciences 
Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes 
Illustrates social science and data science principles through real-world problems 
Links computer science concepts to practical social science research 
Promotes good scientific practice 
Provides freely available with data, code, and practical programming exercises, through Binder and GitHub\nNew to the Second Edition:\nIncreased use of examples from different areas of social sciences 
New chapter on dealing with Bias and Fairness in Machine Learning models 
Expanded chapters focusing on Machine Learning and Text Analysis 
Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter\nThis classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner. 
 Review 
"Like the first edition, the new edition will continue to play an important role for the intended audience and a wider professional community. The much-needed second edition is timely and showcases a wide range of examples and application examples from different areas of the social sciences to demonstrate how the methods are implemented using several real datasets. As expected with this kind of book, the topics of this text are diverse in nature, but interesting none the less. As it is well known, machine learning techniques are subject to inherited bias in model selection and consequently negatively impacts post estimation and prediction. This new edition includes a new chapter on dealing with bias and fairness in machine learning models, a much-needed fair and welcome edition! Further, the authors have done an excellent job in expanding the material on machine learning and text analysis. Like the first edition, the main strength of the book is that it offers a wide variety of applications that are based on real datasets emerging from social science perspectives and useful for both academic and professional communes. As Jupyter has become more popular as the data scientists’ computational notebook of choice, the book has new and improved hands-on Jupyter notebooks to complement each chapter’s material. In conclusion, this new edition has an impressive collection of material on useful and interesting topics on big data. The book will be equally useful to graduate students and researchers interested in gaining perspectives and knowledge on this important topic. The new volume comprises of a wealth of information, a kind of one-stop shop, and can be served as a textbook and research reference book."- S. Ejaz Ahmed, Brock University, Canada\nPraise For First Edition 
"This book builds a nice bridge connecting social science and big data methodology. Big data such as social media and electronic health records,

This book explains approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed.

Practical Tools for Designing and Weighting Survey Samples (Statistics for Social and Behavioral Sciences, 51)

Politics & Social Sciences

Valliant, Richard

Dever, Jill A.

Sociology

Springer

Data Analysis Using Stata provides a comprehensive introduction to Stata with an emphasis on data management, linear regression, logistic modeling, and using programs to automate repetitive tasks. Throughout the book, the authors make extensive use of examples using data from the German Socioeconomic Panel, a large survey of households containing demographic, income, employment, and other key information. The book begins with an introduction to the Stata interface and then proceeds with a discussion of Stata syntax and simple programming tools like foreach loops. The core of the book includes chapters on producing tables and graphs, performing linear regression, and using logistic regression. The remainder of the book includes chapters on reading text files, writing programs and ado-files, and Internet resources, such as the search command and the SSC archive. All key concepts are illustrated with multiple examples. Data Analysis Using Stata will serve as a valuable introduction to Stata, both for those who are new to statistics and statistical computing as well as for those new to Stata but familiar with other programs.

Data Analysis Using Stata

Science & Math

Kohler, Ulrich

Mathematics

Stata Press

Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.

Data Analysis Using Stata, Third Edition

The goal of this book is to put an array of tools at the fingertips of students, practitioners, and researchers by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This volume serves at least three audiences: (1) students of applied sampling techniques; 2) practicing survey statisticians applying concepts learned in theoretical or applied sampling courses; and (3) social scientists and other survey practitioners who design, select, and weight survey samples. The text thoroughly covers fundamental aspects of survey sampling, such as sample size calculation (with examples for both single- and multi-stage sample design) and weight computation, accompanied by software examples to facilitate implementation. Features include step-by-step instructions for calculating survey weights, extensive real-world examples and applications, and representative programming code in R, SAS, and other packages. Since the publication of the first edition in 2013, there have been important developments in making inferences from nonprobability samples, in address-based sampling (ABS), and in the application of machine learning techniques for survey estimation. New to this revised and expanded edition: • Details on new functions in the PracTools package • Additional machine learning methods to form weighting classes • New coverage of nonlinear optimization algorithms for sample allocation • Reflecting effects of multiple weighting steps (nonresponse and calibration) on standard errors • A new chapter on nonprobability sampling • Additional examples, exercises, and updated references throughout Richard Valliant, PhD, is Research Professor Emeritus at the Institute for Social Research at the University of Michigan and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an Associate Editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology. Jill A. Dever, PhD, is Senior Research Statistician at RTI International in Washington, DC. She is a Fellow of the American Statistical Association, Associate Editor for Survey Methodology and the Journal of Official Statistics, and an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. She has served on several panels for the National Academy of Sciences and as a task force member for the American Association of Public Opinion Research’s report on nonprobability sampling. Frauke Kreuter, PhD, is Professor and Director of the Joint Program in Survey Methodology at the University of Maryland, Professor of Statistics and Methodology at the University of Mannheim, and Head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nürnberg, Germany. She is a Fellow of the American Statistical Association and has been Associate Editor of the Journal of the Royal Statistical Society, Journal of Official Statistics, Sociological Methods and Research, Survey Research Methods, Public Opinion Quarterly, American Sociological Review, and the Stata Journal. She is founder of the International Program for Survey and Data Science and co-founder of the Coleridge Initiative.

Practical Tools for Designing and Weighting Survey Samples (Statistics for Social and Behavioral Sciences)

This book is conceived as an introduction to the statistics program Stata and to the techniques of data analysis. In addition to a didactic reformulation of the term ""missing value,"" the new edition contains adaptations to the latest version of the Stata program (version 14).

Datenanalyse mit Stata: Allgemeine Konzepte der Datenanalyse und ihre praktische Anwendung (German Edition)

Business & Money

Education & Reference

De Gruyter Oldenbourg

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