Social Science Literature

Doing a Literature Search provides a practical and comprehensive guide to searching the literature on any topic within the social sciences. The book will enable the reader to search the literature effectively, identifying useful books, articles, statistics and many other sources of information. The text will be an invaluable research tool for postgraduates and researchers across the social sciences.

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.

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.

Such diverse thinkers as Lao-Tze, Confucius, and U.S. Defense Secretary Donald Rumsfeld have all pointed out that we need to be able to tell the difference between real and assumed knowledge. The systematic review is a scientific tool that can help with this difficult task. It can help, for example, with appraising, summarising, and communicating the results and implications of otherwise unmanageable quantities of data. This book, written by two highly-respected social scientists, provides an overview of systematic literature review methods: Outlining the rationale and methods of systematic reviews; Giving worked examples from social science and other fields; Applying the practice to all social science disciplines; It requires no previous knowledge, but takes the reader through the process stage by stage; Drawing on examples from such diverse fields as psychology, criminology, education, transport, social welfare, public health, and housing and urban policy, among others. Including detailed sections on assessing the quality of both quantitative, and qualitative research; searching for evidence in the social sciences; meta-analytic and other methods of evidence synthesis; publication bias; heterogeneity; and approaches to dissemination.

This clear, straightforward textbook embraces the practical reality of actually doing fieldwork. It tackles the common problems faced by new researchers head on, offering sensible advice and instructive case studies from the author’s own experience. Barbara Czarniawska takes us on a master class through the research process, encouraging us to revisit the various facets of the fieldwork research and helping us to reframe our own experiences. Combining a conversational style of writing with an impressive range of empirical examples she takes the reader from planning and designing research to collecting and analyzing data all the way to writing up and disseminating findings. This is a sophisticated introduction to a broad range of research methods and methodologies; it will be of great interest to anyone keen to revisit social research in the company of an expert guide.

"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"--

"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--

Focusing on the integral role of the researcher, Qualitative Research for the Social Sciences uses a conversational writing style that draws readers into the excitement of the research process. Lichtman offers a balanced and nuanced approach, covering the full range of qualitative methodologies and viewpoints about the field, including coverage of social media as a tool to facilitate research or as a venue for study. After presenting theoretical concepts and a historical overview, Lichtman guides readers, step by step, through the research process, addressing issues of analyzing data, presenting completed research, and evaluating research. Real-world examples from across the social sciences provide both practical and theoretical information, helping readers understand abstract ideas and apply them to their own research.

Research that has been presented primarily by quantitative research can benefit from the voice of the participants and the added value of the different perspective that qualitative research can provide. The purpose of mixed methods research is to draw from the positive aspects of both research paradigms to better answer the research question. This type of research is often used in schools, businesses, and non-profit organizations as they strive to address and resolve questions that will impact their organizations. Applied Social Science Approaches to Mixed Methods Research is an academic research publication that examines more traditional and common research methods and how they can be complimented through qualitative counterparts. The content within this publication covers an array of topics such as entrepreneurship, social media, and marginalization. It is essential for researchers, academicians, non-profit professionals, business professionals, and higher education faculty, and specifically targets master or doctoral students committed to writing their theses, dissertations, or scholarly articles, who may not have had the benefit of working on a traditional research team.

Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both published and unpublished studies, create a coding manual, use traditional and unique effect size indices, and write a meta-analytic review. An ongoing example illustrates meta-analytic techniques. In addition to the fundamentals, the book discusses more advanced topics, such as artifact correction, random- and mixed-effects models, structural equation representations, and multivariate procedures. User-friendly features include annotated equations; discussions of alternative approaches; and "Practical Matters" sections that give advice on topics not often discussed in other books, such as linking meta-analytic results with theory and the utility of meta-analysis software programs. ÿ