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| Author | : |
| Publisher | : Frontiers E-books |
| Total Pages | : 158 |
| Release | : |
| Genre | : |
| ISBN | : 2889191559 |
| Author | : Jason W. Osborne |
| Publisher | : SAGE |
| Total Pages | : 297 |
| Release | : 2013 |
| Genre | : Mathematics |
| ISBN | : 1412988012 |
Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process of examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-based and will motivate change in practice by empirically demonstrating, for each topic, the benefits of following best practices and the potential consequences of not following these guidelines. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook will be indispensible.
| Author | : Katy Huxley |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : Anthropology |
| ISBN | : 9781529748147 |
Data cleaning is the process of quality checking quantitative data to ensure a data set contains accurate information. Data cleaning involves a number of practical approaches to dealing with data such as checking data coding, checking data inputting, examining data distributions, and identifying issues such as extreme values. Data cleaning may be an important step of the research process in order to meet statistical assumptions for analytic techniques and is particularly important to reduce the impact of any errors made during data collection or imputation. This entry provides a detailed overview of data cleaning processes and techniques to support accurate and reliable data analysis. This includes screening data, dealing with extreme values, dealing with missing or incomplete data, and data distributions. Data cleaning is not an objective exercise and subjective decisions may need to be made during the data cleaning process. It is imperative that researchers are transparent about data cleaning processes and decisions.
| Author | : David Whitman |
| Publisher | : |
| Total Pages | : 390 |
| Release | : 2008 |
| Genre | : Academic achievement |
| ISBN | : |
This book tells the story of six secondary schools that have succeeded in eliminating or dramatically shrinking the achievement gap between whites and disadvantaged black and Hispanic students. It recounts the stories of the University Park Campus School (UPCS) in Worcester, the American Indian Public Charter School in Oakland, Amistad Academy in New Haven, the Cristo Rey Jesuit High School in Chicago, the KIPP Academy in the Bronx, and the SEED school in Washington, D.C.
| Author | : Steven S. Skiena |
| Publisher | : Springer |
| Total Pages | : 456 |
| Release | : 2017-07-01 |
| Genre | : Computers |
| ISBN | : 3319554441 |
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
| Author | : |
| Publisher | : |
| Total Pages | : 140 |
| Release | : 2000-01 |
| Genre | : |
| ISBN | : |
Popular Mechanics inspires, instructs and influences readers to help them master the modern world. Whether it’s practical DIY home-improvement tips, gadgets and digital technology, information on the newest cars or the latest breakthroughs in science -- PM is the ultimate guide to our high-tech lifestyle.
| Author | : Jason W. Osborne |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : 0 |
| Release | : 2014-07-23 |
| Genre | : Factor analysis |
| ISBN | : 9781500594343 |
Best Practices in Exploratory Factor Analysis (EFA) is a practitioner-oriented look at this popular and often-misunderstood statistical technique. We avoid formulas and matrix algebra, instead focusing on evidence-based best practices so you can focus on getting the most from your data.Each chapter reviews important concepts, uses real-world data to provide authentic examples of analyses, and provides guidance for interpreting the results of these analysis. Not only does this book clarify often-confusing issues like various extraction techniques, what rotation is really rotating, and how to use parallel analysis and MAP criteria to decide how many factors you have, but it also introduces replication statistics and bootstrap analysis so that you can better understand how precisely your data are helping you estimate population parameters. Bootstrap analysis also informs readers of your work as to the likelihood of replication, which can give you more credibility. At the end of each chapter, the author has recommendations as to how to enhance your mastery of the material, including access to the data sets used in the chapter through his web site. Other resources include syntax and macros for easily incorporating these progressive aspects of exploratory factor analysis into your practice. The web site will also include enrichment activities, answer keys to select exercises, and other resources. The fourth "best practices" book by the author, Best Practices in Exploratory Factor Analysis continues the tradition of clearly-written, accessible guides for those just learning quantitative methods or for those who have been researching for decades.NEW in August 2014! Chapters on factor scores, higher-order factor analysis, and reliability. Chapters: 1 INTRODUCTION TO EXPLORATORY FACTOR ANALYSIS 2 EXTRACTION AND ROTATION 3 SAMPLE SIZE MATTERS 4 REPLICATION STATISTICS IN EFA 5 BOOTSTRAP APPLICATIONS IN EFA 6 DATA CLEANING AND EFA 7 ARE FACTOR SCORES A GOOD IDEA? 8 HIGHER ORDER FACTORS 9 AFTER THE EFA: INTERNAL CONSISTENCY 10 SUMMARY AND CONCLUSIONS
| Author | : Joseph Henrich |
| Publisher | : Princeton University Press |
| Total Pages | : 464 |
| Release | : 2017-10-17 |
| Genre | : Psychology |
| ISBN | : 0691178437 |
How our collective intelligence has helped us to evolve and prosper Humans are a puzzling species. On the one hand, we struggle to survive on our own in the wild, often failing to overcome even basic challenges, like obtaining food, building shelters, or avoiding predators. On the other hand, human groups have produced ingenious technologies, sophisticated languages, and complex institutions that have permitted us to successfully expand into a vast range of diverse environments. What has enabled us to dominate the globe, more than any other species, while remaining virtually helpless as lone individuals? This book shows that the secret of our success lies not in our innate intelligence, but in our collective brains—on the ability of human groups to socially interconnect and learn from one another over generations. Drawing insights from lost European explorers, clever chimpanzees, mobile hunter-gatherers, neuroscientific findings, ancient bones, and the human genome, Joseph Henrich demonstrates how our collective brains have propelled our species' genetic evolution and shaped our biology. Our early capacities for learning from others produced many cultural innovations, such as fire, cooking, water containers, plant knowledge, and projectile weapons, which in turn drove the expansion of our brains and altered our physiology, anatomy, and psychology in crucial ways. Later on, some collective brains generated and recombined powerful concepts, such as the lever, wheel, screw, and writing, while also creating the institutions that continue to alter our motivations and perceptions. Henrich shows how our genetics and biology are inextricably interwoven with cultural evolution, and how culture-gene interactions launched our species on an extraordinary evolutionary trajectory. Tracking clues from our ancient past to the present, The Secret of Our Success explores how the evolution of both our cultural and social natures produce a collective intelligence that explains both our species' immense success and the origins of human uniqueness.
| Author | : Riel Miller |
| Publisher | : Routledge |
| Total Pages | : 276 |
| Release | : 2018-04-27 |
| Genre | : Nature |
| ISBN | : 1351047981 |
People are using the future to search for better ways to achieve sustainability, inclusiveness, prosperity, well-being and peace. In addition, the way the future is understood and used is changing in almost all domains, from social science to daily life. This book presents the results of significant research undertaken by UNESCO with a number of partners to detect and define the theory and practice of anticipation around the world today. It uses the concept of ‘Futures Literacy’ as a tool to define the understanding of anticipatory systems and processes – also known as the Discipline of Anticipation. This innovative title explores: • new topics such as Futures Literacy and the Discipline of Anticipation; • the evidence collected from over 30 Futures Literacy Laboratories and presented in 14 full case studies; • the need and opportunity for significant innovation in human decision-making systems. This book will be of great interest to scholars, researchers, policy-makers and students, as well as activists working on sustainability issues and innovation, future studies and anticipation studies. The Open Access version of this book, available at https://www.taylorfrancis.com/books/e/9781351047999, has been made available under a Attribution-NonCommercial-NoDerivs 3.0 IGO (CC-BY-NC-ND 3.0 IGO) license.
| Author | : Jason W. Osborne |
| Publisher | : SAGE |
| Total Pages | : 609 |
| Release | : 2008 |
| Genre | : Social Science |
| ISBN | : 1412940656 |
The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.
