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 : 2021-11-30
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 2021-11-30 with categories.
This book presents a complete exploration of statistical data analysis in R for a wide variety of social science disciplines and quantitative methods courses.
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.
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.
Big Data And Social Science
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Author : Ian Foster
language : en
Publisher: CRC Press
Release Date : 2016-08-10
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 2016-08-10 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.
Quantitative Social Science
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Author : Kosuke Imai
language : en
Publisher: Princeton University Press
Release Date : 2021-03-16
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 2021-03-16 with Political Science categories.
"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--
Challenges And Applications Of Data Analytics In Social Perspectives
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Author : Sathiyamoorthi, V.
language : en
Publisher: IGI Global
Release Date : 2020-12-04
Challenges And Applications Of Data Analytics In Social Perspectives written by Sathiyamoorthi, V. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.
With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.
Categorical Data Analysis For The Behavioral And Social Sciences
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Author : Razia Azen
language : en
Publisher: Taylor & Francis
Release Date : 2021-05-26
Categorical Data Analysis For The Behavioral And Social Sciences written by Razia Azen and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-26 with Psychology categories.
Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer. Each chapter begins with a "Look Ahead" section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge. New to the second edition: The addition of R syntax for all analyses and an update of SPSS and SAS syntax. The addition of a new chapter on GLMMs. Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters. Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book’s accessible approach.
Theory Based Data Analysis For The Social Sciences
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Author : Carol S. Aneshensel
language : en
Publisher: SAGE
Release Date : 2013
Theory Based Data Analysis For The Social Sciences written by Carol S. Aneshensel and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Reference categories.
This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.
Handbook Of Computational Social Science Volume 2
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Author : Uwe Engel
language : en
Publisher: Taylor & Francis
Release Date : 2021-11-10
Handbook Of Computational Social Science Volume 2 written by Uwe Engel and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-10 with Computers categories.
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.
Big Data In Computational Social Science And Humanities
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Author : Shu-Heng Chen
language : en
Publisher: Springer
Release Date : 2018-11-29
Big Data In Computational Social Science And Humanities written by Shu-Heng Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-29 with Computers categories.
This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities.