Data Analytics For The Social Sciences
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Data Analytics For The Social Sciences
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Author : G. David Garson
language : en
Publisher: Routledge
Release Date : 2021-11-29
Data Analytics For The Social Sciences written by G. David Garson and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Psychology categories.
Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.
Data Science And Social Research
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Author : N. Carlo Lauro
language : en
Publisher: Springer
Release Date : 2017-11-17
Data Science And Social Research written by N. Carlo Lauro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-17 with Social Science categories.
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.
Quantitative Social Science
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Author : Kosuke Imai
language : en
Publisher: Princeton University Press
Release Date : 2018-02-09
Quantitative Social Science written by Kosuke Imai and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-09 with Social Science categories.
An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields 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 Looking for a more accessible introduction? Consider Data Analysis for Social Science by Elena Llaudet and Kosuke Imai, which teaches from scratch and step-by-step the fundamentals of survey research, predictive models, and causal inference. It covers descriptive statistics, the difference-in-means estimator, simple linear regression, and multiple linear regression.
Big Data And Social Science
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Author : Ian Foster
language : en
Publisher: CRC Press
Release Date : 2016-09-15
Big Data And Social Science written by Ian Foster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-15 with Mathematics categories.
Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.
Exploring Data
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Author : Catherine Marsh
language : en
Publisher:
Release Date : 1988
Exploring Data written by Catherine Marsh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Social sciences categories.
Qualitative Data Analysis
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Author : Ian Dey
language : en
Publisher: Routledge
Release Date : 2003-09-02
Qualitative Data Analysis written by Ian Dey and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-09-02 with Psychology categories.
First Published in 2004. Learning how to analyze qualitative data by computer can be fun. That is one assumption underpinning this introduction to qualitative analysis, which takes account of how computing techniques have enhanced and transformed the field. The author provides a practical discussion of the main procedures for analyzing qualitative data by computer, with most of its examples taken from humour or everyday life. He examines ways in which computers can contribute to greater rigour and creativity, as well as greater efficiency in analysis. He discusses some of the pitfalls and paradoxes as well as the practicalities of computer-based qualitative analysis. The perspective of "Qualitative Data Analysis" is pragmatic rather than prescriptive, introducing different possibilities without advocating one particular approach. The result is a largely discipline-neutral text, which is suitable for arts and social science students and first-time qualitative analysts.
Big Data Research For Social Sciences And Social Impact
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Author : Miltiadis D. Lytras
language : en
Publisher: MDPI
Release Date : 2020-03-19
Big Data Research For Social Sciences And Social Impact written by Miltiadis D. Lytras and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-19 with Technology & Engineering categories.
A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.
Using R For Data Analysis In Social Sciences
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Author : Quan Li
language : en
Publisher: Oxford University Press
Release Date : 2018-05-09
Using R For Data Analysis In Social Sciences written by Quan Li and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-09 with Political Science categories.
Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.
Big Data And Social Science
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Author : Ian Foster
language : en
Publisher: CRC Press
Release Date : 2020-11-17
Big Data And Social Science written by Ian Foster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-17 with Mathematics categories.
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: 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.
Introducing Data Analysis For Social Scientists
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Author : David Rose
language : en
Publisher:
Release Date : 1993-01-01
Introducing Data Analysis For Social Scientists written by David Rose and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-01-01 with Social Science categories.