Download Free Current Studies In Social Sciences Ii Book in PDF and EPUB Free Download. You can read online Current Studies In Social Sciences Ii and write the review.

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.
The use of case studies to build and test theories in political science and the other social sciences has increased in recent years. Many scholars have argued that the social sciences rely too heavily on quantitative research and formal models and have attempted to develop and refine rigorous methods for using case studies. This text presents a comprehensive analysis of research methods using case studies and examines the place of case studies in social science methodology. It argues that case studies, statistical methods, and formal models are complementary rather than competitive. The book explains how to design case study research that will produce results useful to policymakers and emphasizes the importance of developing policy-relevant theories. It offers three major contributions to case study methodology: an emphasis on the importance of within-case analysis, a detailed discussion of process tracing, and development of the concept of typological theories. Case Studies and Theory Development in the Social Sciences will be particularly useful to graduate students and scholars in social science methodology and the philosophy of science, as well as to those designing new research projects, and will contribute greatly to the broader debate about scientific methods.
The impact agenda is set to shape the way in which social scientists prioritise the work they choose to pursue, the research methods they use and how they publish their findings over the coming decade, but how much is currently known about how social science research has made a mark on society? Based on a three year research project studying the impact of 360 UK-based academics on business, government and civil society sectors, this groundbreaking new book undertakes the most thorough analysis yet of how academic research in the social sciences achieves public policy impacts, contributes to economic prosperity, and informs public understanding of policy issues as well as economic and social changes. The Impact of the Social Sciences addresses and engages with key issues, including: identifying ways to conceptualise and model impact in the social sciences developing more sophisticated ways to measure academic and external impacts of social science research explaining how impacts from individual academics, research units and universities can be improved. This book is essential reading for researchers, academics and anyone involved in discussions about how to improve the value and impact of funded research.
To do research that really makes a difference -- the authors of this book argue -- social scientists need a diverse set of questions and methods, both qualitative and quantitative, in order to reflect the complexity of the world. Bringing together a consortium of voices across a variety of fields, Methods That Matter offers compelling and successful examples of mixed methods research that does just that. Discussing their own endeavors to combine quantitative and qualitative methodologies, the authors invite readers into a conversation about the best designs and practices of mixed methods to stimulate creative ideas and find new pathways of insight. The result is an engaging exploration of a promising approach to the social sciences. --
Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them. Understanding that social science research has real consequences for individuals when used by professionals in public policy, health, law enforcement, and other fields, the book crystallizes new insights, practices, and methods that help ensure greater research transparency, openness, and reproducibility. Readers are guided through well-known problems and are encouraged to work through new solutions and practices to improve the openness of their research. Created with both experienced and novice researchers in mind, Transparent and Reproducible Social Science Research serves as an indispensable resource for the production of high quality social science research.
The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019.
The European social sciences tend to absorb criticism that has been passed on the European approach and re-label it as a part of what the critique opposes; criticism of European social sciences by “subaltern” social sciences, their “talking back”, has become a frequent line of reflection in European social sciences. The re-labelling of the critique of the European approach to social sciences towards a critique from “Southern” social sciences of “Western” social sciences has somehow turned “Southern” as well as “Western” social sciences into competing contributors to the same “globalizing” social sciences. Both are no longer arguing about the European approach to social sciences but about which social thought from which part of the globe prevails. If the critique becomes a part of what it opposes, one might conclude that the European social sciences are very adaptable and capable of learning. One might, however, also raise the question whether there is anything wrong with the criticism of the European social sciences; or, for that matter, whether there is anything wrong with the European social sciences themselves. The contributions in this book discuss these questions from different angles: They revisit the mainstream critique of the European social sciences, and they suggest new arguments criticizing social science theories that may be found as often in the “Western” as in the “Southern” discourse.
This volume presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These methods include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. This volume contains the following five sections: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models). Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.
First multi-year cumulation covers six years: 1965-70.