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Conducting Mixed-Methods Research: From Classical Social Sciences to the Age of Big Data and Analytics
Viswanath Venkatesh; Susan (Sue) Brown; Yulia Sullivan · Virginia Tech Libraries · 2023
Scholars in the social sciences are increasingly expected to incorporate both quantitative and qualitative techniques and methods into their research. The growth of “mixed-methods” research is evident in social science disciplines ranging from psychology and management to marketing and information systems. This book is designed to provide principles, strategies, and guidance specifically for researchers in these disciplines so that they can use mixed-methods research more effectively. In thirteen chapters, Conducting Mixed-Methods Research takes readers through the research process, from defining research questions to writing articles using a mixed-methods approach. For those who are well trained in either qualitative or quantitative methods, the book shows them how to think about the purposes of mixed-methods research, design mixed-methods studies, and develop meta-inferences by integrating findings from both methods. Throughout, the discussion is grounded in examples taken from published research, carefully chosen to highlight best practices, thus opening a window into a broad body of mixed-methods research applications.
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Social Data Analysis
Mikaila Arthur; Roger Clark · Rhode Island College Digital Publishing · 2021
Social data analysis enables you, as a researcher, to organize the facts you collect during your research. Your data may have come from a questionnaire survey, a set of interviews, or observations. They may be data that have been made available to you from some organization, national or international agency or other researchers. Whatever their source, social data can be daunting to put together in a way that makes sense to you and others. This book is meant to help you in your initial attempts to analyze data. In doing so it will introduce you to ways that others have found useful in their attempts to organize data. You might think of it as like a recipe book, a resource that you can refer to as you prepare data for your own consumption and that of others. And, like a recipe book that teaches you to prepare simple dishes, you may find this one pretty exciting. Analyzing data in a revealing way is at least as rewarding, we’ve found, as it is to cook up a yummy cashew carrot paté or a steaming corn chowder. We’d like to share our pleasure with you.
![The Data Renaissance: Analyzing the Disciplinary Effects of Big Data, Artificial Intelligence, and Beyond [Revised Edition]](/api/files/covers/otl-1718.webp)
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The Data Renaissance: Analyzing the Disciplinary Effects of Big Data, Artificial Intelligence, and Beyond [Revised Edition]
J.J. Sylvia IV · ROTEL · 2024
The Data Renaissance delves into the complexities of data's role in various industries and its broader impact on society. It highlights the challenges in investigating data practices, citing examples like TikTok, where algorithms and data handling are closely guarded secrets. The content, contributed by students under the guidance of an expert, covers a wide range of topics, including the ethical aspects of generative AI in education and the workplace, and case studies reflecting real-world experiences. This evolving text, intended to be updated with each class, serves as a dynamic resource for educators and students alike, offering insights and discussion guides for an in-depth understanding of the ever-changing landscape of data in our digital age.
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Semantic Web Development
Sean Burns · Sean Burns · 2025
This book serves as an introduction to semantic web development. A more advanced semantic web development course would introduce students to RDFS, SKOS, OWL, SPARQL or more. But this book expects undergraduate students who are new to web development. Therefore this work focuses on entry level topics: HTML5 and its semantic elements, CSS3, and JSON-LD with schema.org.
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Artificial Intelligence and Librarianship
Martin Frické · SoftOption · 2024
Courses on Artificial Intelligence (AI) and Librarianship in ALA-accredited Masters of Library and Information (MLIS) degrees are rare. We have all been surprised by ChatGPT and similar Large Language Models. Generative AI is an important new area for librarianship. It is also developing so rapidly that no one can really keep up. Those trying to produce AI courses for the MLIS degree need all the help they can get. This book is a gesture of support. It consists of about 100,000 words on the topic, with a 4-500 item bibliography. The third edition has changes and additions. These include: a new Chapter 6 on evaluation and the future new materials in Chapter 5 on current large language and multimodal models scattered revisions, corrections, and updates.
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The Discipline of Organizing: 4th Professional Edition
Robert Glushko · University of California, Berkeley · 2013
We organize things, we organize information, we organize information about things, and we organize information about information. But even though “organizing” is a fundamental and ubiquitous challenge, when we compare these activities their contrasts are more apparent than their commonalities. We propose to unify many perspectives about organizing with the concept of an Organizing System, defined as an intentionally arranged collection of resources and the interactions they support. Every Organizing System involves a collection of resources, a choice of properties or principles used to describe and arrange resources, and ways of supporting interactions with resources. By comparing and contrasting how these activities take place in different contexts and domains, we can identify patterns of organizing. We can create a discipline of organizing in a disciplined way. The 4th edition builds a bridge between organizing and data science. It reframes descriptive statistics as organizing techniques, expands the treatment of classification to include computational methods, and incorporates many new examples of data-driven resource selection, organization, maintenance, and personalization. It introduces a new “data science” category of discipline-specific content, both in the chapter text and in endnotes, marked with [DS] in editions that contain endnotes.
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Think Complexity: Exploring Complexity Science with Python
Allen Downey · Green Tea Press · 2018
Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.