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Author: Perry, S. Marshall
Publisher: IGI Global
ISBN: 1522536175
Category : Social Science
Languages : en
Pages : 349
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Book Description
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.
Author: Perry, S. Marshall
Publisher: IGI Global
ISBN: 1522536175
Category : Social Science
Languages : en
Pages : 349
Get Book Here
Book Description
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.
Author: Kosuke Imai
Publisher: Princeton University Press
ISBN: 0691191093
Category : Political Science
Languages : en
Pages : 464
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Book Description
"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"--
Author: Elena Llaudet
Publisher: Princeton University Press
ISBN: 0691199434
Category : Computers
Languages : en
Pages : 256
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Book Description
"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"--
Author: Jose Manuel Magallanes Reyes
Publisher: Cambridge University Press
ISBN: 1107117410
Category : Computers
Languages : en
Pages : 317
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Book Description
This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.
Author: Mehmet Mehmetoglu
Publisher: SAGE
ISBN: 1529788463
Category : Social Science
Languages : en
Pages : 421
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Book Description
Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: · Original case studies and data sets · Practical exercises and lists of commands for each chapter · Downloadable Stata programmes created to work alongside chapters · A wide range of detailed applications using Stata · Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309037492
Category : Science
Languages : en
Pages : 301
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Book Description
This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research.
Author: Keenan A. Pituch
Publisher: Routledge
ISBN: 1317805925
Category : Psychology
Languages : en
Pages : 814
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Book Description
Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally. Features new to this edition include: NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3) A free online resources site at www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises) Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.
Author: Earl R. Babbie
Publisher: Pine Forge Press
ISBN: 1412982448
Category : Social Science
Languages : en
Pages : 457
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Book Description
Click on the Supplements tab above for further details on the different versions of SPSS programs.
Author: Jure Leskovec
Publisher: Cambridge University Press
ISBN: 1107077230
Category : Computers
Languages : en
Pages : 480
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Book Description
Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
Author: Paolo Mariani
Publisher: Springer Nature
ISBN: 3030512223
Category : Social Science
Languages : en
Pages : 391
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Book Description
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.
