Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.
Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research.
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.
Visual Research Methods: Image, Society, and Representation addresses the growing question in social research of how to critically incorporate visual data and visual methodologies in ways that expand and enhance the researcher′s repertoire for understanding and teaching about the social world. Editor Gregory C. Stanczak crisscrosses disciplines in ways that highlight the multiple manifestations of this newer interdisciplinary trend. Beyond methodological interests, the rich diversity of subject matter provides this volume′s pedagogical punch. Key Features Provides a valuable framework for classroom use and comparative analysis: Organized around three themes in visual research—methodology, epistemological reflection, and theoretical contribution of images Addresses a wide range of topics: Original and reprinted works by leading qualitative researchers from various fields, including Sociology, Education, Political Science, Religion, History, and Gender Studies Offers a roadmap to common issues and topics: Reader′s guide connects different chapters to different conceptual themes and methodological approaches Presents vivid visual data: Methodologies go beyond photography alone and include video and virtual research Intended Audience: This is an excellent text for advanced undergraduate and graduate courses in social research across disciplines such as Sociology, Education, Cultural Studies, Anthropology, American Studies, Communications, Gender Studies, and Political Science.Vi
This book integrates social science research methods and the descriptions of 46 univariate, bivariate, and multivariate tests to include a description of the purpose, assumptions, example research question and hypothesis, SPSS procedure, and interpretation of SPSS output for each test. Included throughout the book are various sidebars highlighting key points, images and SPSS screenshots to assist understanding the material presented, self-test reviews at the end of each chapter, a decision tree to facilitate identification of the proper statistical test, examples of SPSS output with accompanying analysis and interpretations, links to relevant web sites, and a comprehensive glossary. Underpinning all these features is a concise, easy to understand explanation of the material.
Provides: an historical overview of the development of the narrative approach; a guide to how narrative methods can be applied in fieldwork; how to incorporate a narrative approach within a field project; guidelines for interpreting collected or produced narratives; and useful guides for further reading.
Making research in all fields of study readily available is imperative in order to circulate new information and upcoming trends. This is possible through the efficient utilization of collections of information. Maximizing Social Science Research Through Publicly Accessible Data Sets is an essential reference source for the latest academic perspectives on a wide range of methodologies and large data sets with the purpose of enhancing research in the areas of human society and social relationships. Featuring coverage on a broad range of topics such as student achievement, teacher efficacy, and instructional leadership, this book is ideally designed for academicians, researchers, and practitioners seeking material on the availability and distribution methods of research content.
This introductory text presents basic principles of social science research through maps, graphs, and diagrams. The authors show how concept maps and mind maps can be used in quantitative, qualitative, and mixed methods research, using student-friendly examples and classroom-based activities. Integrating theory and practice, chapters show how to use these tools to plan research projects, "see" analysis strategies, and assist in the development and writing of research reports.
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.
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.
