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Friday, July 24, 2020 | History

2 edition of Large-scale data analytic studies in the social sciences found in the catalog.

Large-scale data analytic studies in the social sciences

Paul B. Slater

Large-scale data analytic studies in the social sciences

by Paul B. Slater

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Published by Community and Organization Research Institute in Santa Barbara, CA .
Written in English

    Subjects:
  • Social sciences -- Research.,
  • Sociology -- Methodology.

  • Edition Notes

    Statementby Paul B. Slater.
    Classifications
    LC ClassificationsHM48 S54 1986
    The Physical Object
    Pagination153 p. :
    Number of Pages153
    ID Numbers
    Open LibraryOL16548196M

      Accessible, engaging, and informative, this text 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 s: 5. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research.

      Perhaps some social scientists are resistant to social media data analytics because the methods differ from more traditional social science research. However, most Twitter analytic tools enable multiple research methods such as social network analysis, geographic analysis, content analysis, and textual hermeneutics and more.   The book is a tour de force in making data analysis manageable and rewarding for today′s undergraduate studying research methods. `I′m full of admiration for this book. Once again, David de Vaus has come up with a superb book that is well written and organized and which will be a boon to a wide range of s: 2.

    2 ANALYTICS IN A BIG DATA WORLD exploit big data. In anot her poll ran by KDnu ggets in Ju ly , a stron g need emerged for analytics/big data/data mining/data science educa-tion.4 It is the purpose of this book to try and fi ll this gap by providing a concise and focused overview of analytics for the business practitioner. EXAMPLE APPLICATIONS. The IFRI book, written by North American authors with a North American audience in mind, offers a comprehensive analysis that focuses on accounting for the entire insurance entity, not just its insurance contract. IFRI offers pages of material, plus five product-specific Excel workbooks to amplify the principles covered in the book.


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Large-scale data analytic studies in the social sciences by Paul B. Slater Download PDF EPUB FB2

Large scale data analytic studies in the social sciences. Santa Barbara, CA: Community and Organization Research Institute, University of California, © (OCoLC) Material Type: Government publication, State or province government publication: Document Type: Book: All Authors / Contributors: Paul B Slater.

The principles of analysing data in simple stages are clearly explained, and it provides an introduction to using computers and SPSS, the most widely used statistical package in the social sciences. This second edition includes both WINDOWS and PC+ versions of SPSS, as well as a glossary of statistical by: 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. Toward Computational Social Science: Big Data in Digital Environments The ANNALS of the American Academy of Political and Social Science (1), The articles in this special issue on Big Data and computational social science come from diverse disciplines, including psychology, epidemiology, political science, and communication studies.

Apply statistics to your everyday life. Statistics and Data Analysis for Social Science helps students to build a strong foundational understanding of statistics by providing clarity around when and why statistics useful.

Rather than focusing on the “how to” of statistics, author Eric J. Krieg simplifies the complexity of statistical calculations by introducing only what is necessary to.

Gathering data is the easy part of the empirical research process but often students do not think carefully enough about the analysis of their data before they start to gather it.

This book gives clear guidance on the methodology and process of data analysis giving clear and concise approaches to data analysis methods and tools. Most social scientists set out to solve a real- world social or economic problem: they frame the problem, identify the data, do the analysis, and then draw inferences.

At all points, of course, the social scientist needs to consider the ethical ramifications of their work, particularly respecting privacy and confidentiality.

Gregor Wiedemann evaluates text mining applications for social science studies with respect to conceptual integration of consciously Text Mining for Qualitative Data Analysis in the Social Sciences A Study on Democratic Discourse in Germany.

Authors (view affiliations) Computer-Assisted Text Analysis in the Social Sciences. Gregor. Data analysis is a critical stage in social research. Considering its primary audience—project students at the undergraduate level—the paper covers the basics approaches to analyzing data from social research.

Using simple terms, as much as possible. This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come.

At the dawn of the. 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 s: 3. Venkat N. Gudivada, in Data Analytics for Intelligent Transportation Systems, Introduction.

Data analytics is the science of integrating heterogeneous data from diverse sources, drawing inferences, and making predictions to enable innovation, gain competitive business advantage, and help strategic decision-making.

The data analytics domain has evolved under various names including. Book chapter Full text access Chapter 1 - Document Informatics for Scientific Learning and Accelerated Discovery A Large-Scale Study of Language Usage as a Cognitive Biometric Trait.

Chapter 11 - Experimental Computational Simulation Environments for Big Data Analytic in Social Sciences. Michal Galas. Pages Download PDF. Chapter. R.C. Rockwell, in International Encyclopedia of the Social & Behavioral Sciences, Archives of quantitative social science data began in the s in the United States and have since spread to much of the world.

Two major thrusts underlay this movement: (a) the need for transparency in the conduct of research and (b) the potential of data archives to facilitate comparative and time-series.

terminology of data analysis, and be prepared to learn about using JMP for data analysis. Introduction: A Common Language for Researchers Research in the social sciences is a diverse topic.

In part, this is because the social sciences represent a wide variety of. This book is designed to introduce doctoral and graduate students to the process of scientific research in the social sciences, business, education, public health, and related disciplines.

This book is based on my lecture materials developed over a decade of teaching the doctoral-level class on Research Methods at the University of South Florida. The target audience for this book includes Ph.D.

Data Science and analytics leaders who want to build competencies in data science and big data analytics. About the authors: Dr. Kirk Borne, Principal Data Scientist at Booz Allen Hamilton, supports the Strategic Innovation Group in the area of NextGen analytics and data Science.

He was a professor at George Mason University in the graduate (Ph. Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how Reviews: 8.

This book covers all the concepts that are the building blocks for further learning on statistical methods for social sciences. Unlike Econometrics textbooks such as Wooldridge's "Econometric Analysis of Cross Section and Panel Data," this book keeps maths at the minimum necessary for understanding the topic being s: A research is valid when a conclusion is accurate or true and research design is the conceptual blueprint within which research is conducted.

A scholar for his research, prepare an action plan, it. Ph.D., Educational Psychology, Quantitative Methods, University of Wisconsin-Madison, M.S., Educational Psychology, Quantitative Methods, University of Wisconsin-Madison, My research is related to the application, development, and assessment of quantitative methods in the social and.

While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope. Data science is an umbrella term for a group of fields that are used to mine large datasets.

Data analytics software is a more focused version of this and can even be considered part of the larger.Large Scale Data Analytics Book Description: This book presents a language integrated query framework for big data.

The continuous, rapid growth of data information to volumes of up to terabytes (1, gigabytes) or petabytes (1, gigabytes) means that the need for a system to manage and query information from large scale data sources is.