Products related to Analysis:
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Data Analysis in Qualitative Research : Theorizing with Abductive Analysis
From two experts in the field comes an accessible, how-to guide that will help researchers think more productively about the relation between theory and data at every stage of their work. In Data Analysis in Qualitative Research, Iddo Tavory and Stefan Timmermans provide a how-to guide filled with tricks of the trade for researchers who hope to take excellent qualitative data and transform it into powerful scholarship.In their previous book, Abductive Analysis: Theorizing Qualitative Research, Timmermans and Tavory offered a toolkit for innovative theorizing in the social sciences.In this companion, they go one step further to show how to uncover the surprising revelations that lie waiting in qualitative data—in sociology and beyond. In this book, they lay out a series of tools designed to help both novice and expert scholars see and understand their data in surprising ways.Timmermans and Tavory show researchers how to “stack the deck” of qualitative research in favor of locating surprising findings that may lead to theoretical breakthroughs, whether by engaging with theory, discussing research strategies, or walking the reader through the process of coding data.From beginning to end of a research project, Data Analysis in Qualitative Research helps social scientists pinpoint the most promising paths to take in their approach.
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Quantitative Longitudinal Data Analysis : Research Methods
First published Open Access under a Creative Commons license as What is Quantitative Longitudinal Data Analysis?, this title is now also available as part of the Bloomsbury Research Methods series. Across the social sciences, there is widespread agreement that quantitative longitudinal research designs offer analysts powerful scientific data resources.But, to date, many texts on analysing longitudinal social analysis surveys have been written from a statistical, rather than a social science data analysis perspective and they lack adequate coverage of common practical challenges associated with social science data analyses. This book provides a practical and up-to-date introduction to influential approaches to quantitative longitudinal data analysis in the social sciences.The book introduces definitions and terms, explains the relative attractions of such a longitudinal design, and offers an introduction to the main techniques of analysis, explaining their requirements, statistical properties and their substantive contribution.
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Computational Analysis of Communication
Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing.Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more.Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues.A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results.Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and othersIntegrates theory, methods, and tools to provide unified approach to the subjectIncludes sample code in Python and links to actual research questions and cases from social science and communication studiesDiscusses ethical and normative issues relevant to privacy, data ownership, and reproducible social scienceDeveloped in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.
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Critical Theory and Qualitative Data Analysis in Education
Critical Theory and Qualitative Data Analysis in Education offers a path-breaking explanation of how critical theories can be used within the analysis of qualitative data to inform research processes, such as data collection, analysis, and interpretation.This contributed volume offers examples of qualitative data analysis techniques and exemplars of empirical studies that employ critical theory concepts in data analysis.By creating a clear and accessible bridge between data analysis and critical social theories, this book helps scholars and researchers effectively translate their research designs and findings to multiple audiences for more equitable outcomes and disruption of historical and contemporary inequality.
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What is a communication analysis?
A communication analysis is the process of examining and evaluating the effectiveness of communication within a specific context or organization. This can involve studying the methods, channels, and content of communication to identify strengths, weaknesses, and areas for improvement. Communication analysis can also involve assessing the impact of communication on relationships, decision-making, and overall organizational performance. The goal of communication analysis is to gain insights into how communication functions and to make recommendations for enhancing its effectiveness.
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Does anyone have experience with further education in data analysis?
Yes, I have experience with further education in data analysis. I have completed a certification program in data analysis and have also taken advanced courses in statistics, programming, and data visualization. This education has provided me with the skills and knowledge necessary to work with large datasets, perform complex analysis, and communicate insights effectively. I have also gained practical experience through projects and internships, which have further solidified my understanding of data analysis concepts and techniques.
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How do I write a communication analysis?
To write a communication analysis, start by selecting a communication piece to analyze, such as a speech, advertisement, or conversation. Next, identify the purpose, audience, and context of the communication. Then, analyze the key elements of the communication, such as the message, tone, language, and nonverbal cues. Finally, evaluate the effectiveness of the communication in achieving its intended goals and consider any potential improvements or alternative strategies that could enhance the communication.
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What exactly does a specialist in information technology for data and process analysis do?
A specialist in information technology for data and process analysis is responsible for analyzing and interpreting data to identify trends, patterns, and insights that can be used to improve business processes and decision-making. They use various tools and techniques to collect, clean, and analyze data, and then present their findings to stakeholders in a clear and understandable manner. They also work to identify areas for process improvement and develop strategies to optimize workflows and increase efficiency. Overall, their role is to leverage technology and data to drive informed decision-making and improve business operations.
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Quantitative Data Analysis : Doing Social Research to Test Ideas
This book is an accessible introduction to quantitative data analysis, concentrating on the key issues facing those new to research, such as how to decide which statistical procedure is suitable, and how to interpret the subsequent results.Each chapter includes illustrative examples and a set of exercises that allows readers to test their understanding of the topic.The book, written for graduate students in the social sciences, public health, and education, offers a practical approach to making sociological sense out of a body of quantitative data.The book also will be useful to more experienced researchers who need a readily accessible handbook on quantitative methods. The author has posted stata files, updates and data sets at his website http://tinyurl.com/Treiman-stata-files-data-sets.
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Introduction to Research Methods and Data Analysis in Psychology
This third edition of Introduction to Research Methods and Data Analysis in Psychology provides you with a unique, balanced blend of quantitative and qualitative research methods.Highly practical in nature, the book guides you, step-by-step, through the research process and is underpinned by SPSS screenshots, diagrams and examples throughout.
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Statistics: A Tool for Social Research and Data Analysis
Extremely student friendly, Healey's STATISTICS: A TOOL FOR SOCIAL RESEARCH AND DATA ANALYSIS, 11e, equips you with a solid understanding of statistical fundamentals and their practical application to current social issues -- no advanced math knowledge required.The text breaks down even the most complex material to help you master key concepts and develop the skills you need as a professional in a social science field -- or simply to become a "statistically literate" consumer of social research.Everyday examples illustrate that statistics are not just abstract mathematical constructs, but they have practical value in government, education, business, media, politics, sports and more.Research examples in every chapter include the same "real data" used by professionals across various fields to make evidence-based decisions.Also available: MindTap digital learning solution.
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Quasi-Experimentation : A Guide to Design and Analysis
Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings.Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest–posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs.He details their relative strengths and weaknesses and offers practical advice about their use.Comparing quasi-experiments to randomized experiments, Reichardt discusses when and why the former might be a better choice than the latter in the face of the contingencies that are likely to arise in practice.Modern methods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment.Throughout, mathematical equations are translated into words to enhance accessibility.Adding to its discussion of prototypical quasi-experiments, the book also provides a complete typology of quasi-experimental design options to help the reader craft the best research design to fit the circumstances of a given study.
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How can one study data analysis and data management?
One can study data analysis and data management through a variety of methods. This can include enrolling in formal education programs such as a degree in data science, statistics, or computer science. Additionally, there are many online courses and tutorials available that cover data analysis and data management. Practical experience can also be gained through internships or working on real-world projects. It's important to continuously practice and apply the concepts learned through these methods to gain a deeper understanding of data analysis and data management.
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How can one focus on communication in an analysis?
To focus on communication in an analysis, one can start by clearly defining the purpose and audience of the communication. It is important to identify key messages and ensure they are conveyed effectively. Paying attention to the tone, language, and medium of communication can also help in delivering the message clearly. Additionally, actively listening to feedback and being open to adjusting the communication strategy can further enhance the effectiveness of the analysis.
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What is the topic of the communication analysis exam?
The topic of the communication analysis exam is to evaluate and analyze various forms of communication, such as verbal, nonverbal, written, and visual communication. Students are expected to demonstrate their understanding of communication theories, principles, and concepts, as well as their ability to critically analyze different communication strategies and their effectiveness in various contexts. The exam may also cover topics related to interpersonal communication, organizational communication, mass communication, and the impact of technology on communication.
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How are calculations for circuit analysis in digital technology performed?
Calculations for circuit analysis in digital technology are performed using Boolean algebra and logic gates. Boolean algebra is used to represent the logical relationships between inputs and outputs in a digital circuit. Logic gates, such as AND, OR, and NOT gates, are then used to implement these logical relationships in the circuit. By applying Boolean algebra and using logic gates, engineers can analyze and design digital circuits to ensure proper functionality and performance.
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