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Research Methodology and Data Analysis in Humanities & Social Sciences

Research Methodology and Data Analysis in Humanities & Social Sciences
Author: Rajesh Ekka
Publisher: Lulu.com
Total Pages: 102
Release: 2014-12-16
Genre: Education
ISBN: 1312760125
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Research refers to a search for knowledge. Research is an art of scientific investigation. The Advanced Learner's Dictionary of Current English lays down the meaning of research as, "a careful investigation or inquiry especially through search for new facts in any branch of knowledge".

Humanities Data Analysis

Humanities Data Analysis
Author: Folgert Karsdorp
Publisher: Princeton University Press
Total Pages: 352
Release: 2021-01-12
Genre: Computers
ISBN: 0691172366
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A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations

Social Science Research

Social Science Research
Author: Anol Bhattacherjee
Publisher: CreateSpace
Total Pages: 156
Release: 2012-04-01
Genre: Science
ISBN: 9781475146127
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This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.

Humanities Data in R

Humanities Data in R
Author: Taylor Arnold
Publisher: Springer
Total Pages: 218
Release: 2015-09-23
Genre: Computers
ISBN: 3319207024
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​This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries. ​

Humanities Data Analysis

Humanities Data Analysis
Author: Folgert Karsdorp
Publisher: Princeton University Press
Total Pages: 360
Release: 2021-01-12
Genre: Computers
ISBN: 0691200335
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A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations

Data Analysis for the Social Sciences

Data Analysis for the Social Sciences
Author: Douglas Bors
Publisher: SAGE
Total Pages: 1115
Release: 2018-01-08
Genre: Social Science
ISBN: 1526422301
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′This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.′ —Ruth Horry, Psychology, Swansea University ′This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.′ —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.

Research Methodology for Social Sciences

Research Methodology for Social Sciences
Author: Rajat Acharyya
Publisher: Taylor & Francis
Total Pages: 229
Release: 2019-11-01
Genre: Social Science
ISBN: 1000725782
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Research Methodology for Social Sciences provides guidelines for designing and conducting evidence-based research in social sciences and interdisciplinary studies using both qualitative and quantitative data. Blending the particularity of different sub-disciplines and interdisciplinary nature of social sciences, this volume: Provides insights on epistemological issues and deliberates on debates over qualitative research methods; Covers different aspects of qualitative research techniques and evidence-based research techniques, including survey design, choice of sample, construction of indices, statistical inferences and data analysis; Discusses concepts, techniques and tools at different stages of research, beginning with the design of field surveys to collect raw data and then analyse it using statistical and econometric methods. With illustrations, examples and a reader-friendly approach, this volume will serve as a key reference material for compulsory research methodology courses at doctoral levels across different disciplines, such as economics, sociology, women’s studies, education, anthropology, political science, international relations, philosophy, history and business management. This volume will also be indispensable for postgraduate courses dealing with quantitative techniques and data analysis.

Tracing the Life Cycle of Ideas in the Humanities and Social Sciences

Tracing the Life Cycle of Ideas in the Humanities and Social Sciences
Author: Arjuna Tuzzi
Publisher: Springer
Total Pages: 217
Release: 2018-10-30
Genre: Social Science
ISBN: 331997064X
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This book demonstrates how quantitative methods for text analysis can successfully combine with qualitative methods in the study of different disciplines of the Humanities and Social Sciences (HSS). The book focuses on learning about the evolution of ideas of HSS disciplines through a distant reading of the contents conveyed by scientific literature, in order to retrieve the most relevant topics being debated over time. Quantitative methods, statistical techniques and software packages are used to identify and study the main subject matters of a discipline from raw textual data, both in the past and today. The book also deals with the concept of quality of life of words and aims to foster a discussion about the life cycle of scientific ideas. Textual data retrieved from large corpora pose interesting challenges for any data analysis method and today represent a growing area of research in many fields. New problems emerge from the growing availability of large databases and new methods are needed to retrieve significant information from those large information sources. This book can be used to explain how quantitative methods can be part of the research instrumentation and the "toolbox" of scholars of Humanities and Social Sciences. The book contains numerous examples and a description of the main methods in use, with references to literature and available software. Most of the chapters of the book have been written in a non-technical language for HSS researchers without mathematical, computer or statistical backgrounds.

Data Analysis for Social Science

Data Analysis for Social Science
Author: Elena Llaudet
Publisher: Princeton University Press
Total Pages: 256
Release: 2022-11-29
Genre: Computers
ISBN: 0691199434
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"Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors"--

Research Methods & Data Analysis for Multicultural Social Work and Human Services

Research Methods & Data Analysis for Multicultural Social Work and Human Services
Author: Thanh V. Tran
Publisher:
Total Pages: 340
Release: 2016-08-09
Genre: Social Science
ISBN: 9781516553235
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Research Methods & Data Analysis for Multicultural Social Work and Human Services introduces research methodology to social work students and practitioners. It provides hands-on examples of how to conduct data analysis in SPSS and Stata. It equips readers with the skills needed to become critical research consumers and to engage in agency-based research and evaluation. The text teaches students how to collect appropriate data and analyze data that is suitable for each type of research design. It prepares them to conduct applied social science research in a variety of fields, such as health and mental health, ethnic studies, acculturation, family violence, LGBT studies, and more. Topics addressed include the process of research, ethical issues, the validity and reliability of research instruments, design types, and relevant statistical tools. Research Methods & Data Analysis for Multicultural Social Work and Human Services provides a solid foundation and knowledge base for students and researchers. It is an excellent resource for undergraduate and graduate level research methods and design classes and courses on research and statistics in social work. Thanh V. Tran holds a Ph.D. and a master of science degree in social work from the School of Social Work at the University of Texas, Arlington. Dr. Tran is a professor in the Boston College School of Social Work in Chestnut Hill, Massachusetts. Ce Shen earned a Ph.D. in sociology at Boston College in Chestnut Hill and is now an associate professor in the college's School of Social Work. Siyon Rhee earned a Ph.D. at the School of Social Welfare, University of California, Los Angeles. Dr. Rhee teaches in the School of Social Work at California State University, Los Angeles.