Data Collecting And Analyzing

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.

In simple and non-technical terms, this text illustrates a wide range of techniques and approaches used in social research projects.

The National Children's Study (NCS) is planned to be the largest long-term study of environmental and genetic effects on children's health ever conducted in the United States. It proposes to examine the effects of environmental influences on the health and development of approximately 100,000 children across the United States, following them from before birth until age 21. By archiving all of the data collected, the NCS is intended to provide a valuable resource for analyses conducted many years into the future. This book evaluates the research plan for the NCS, by assessing the scientific rigor of the study and the extent to which it is being carried out with methods, measures, and collection of data and specimens to maximize the scientific yield of the study. The book concludes that if the NCS is conducted as proposed, the database derived from the study should be valuable for investigating hypotheses described in the research plan as well as additional hypotheses that will evolve. Nevertheless, there are important weaknesses and shortcomings in the research plan that diminish the study's expected value below what it might be.

The successful collection of data is a key challenge to obtaining reliable and valid results in applied linguistics research. Data Collection Research Methods in Applied Linguistics investigates how research is conducted in the field, encompassing the challenges and obstacles applied linguists face in collecting good data. The book explores frequently used data collection techniques, including: * interviews and focus groups * observations * stimulated recall and think aloud protocols * data elicitation tasks * corpus methods * questionnaires * validated tests and measures Each chapter focuses on one type of data collection, outlining key concepts, threats to reliability and validity, procedures for good data collection, and implications for researchers. The chapters also include exemplary research projects, showcasing and explaining for readers how the technique was used to collect data in a successfully published study. This book is an essential resource for both novice and experienced applied linguists tackling data collection techniques for the first time.

The Quality Toolbox is a comprehensive reference to a variety of methods and techniques: those most commonly used for quality improvement, many less commonly used, and some created by the author and not available elsewhere. The reader will find the widely used seven basic quality control tools (for example, fishbone diagram, and Pareto chart) as well as the newer management and planning tools. Tools are included for generating and organizing ideas, evaluating ideas, analyzing processes, determining root causes, planning, and basic data-handling and statistics. The book is written and organized to be as simple as possible to use so that anyone can find and learn new tools without a teacher. Above all, this is an instruction book. The reader can learn new tools or, for familiar tools, discover new variations or applications. It also is a reference book, organized so that a half-remembered tool can be found and reviewed easily, and the right tool to solve a particular problem or achieve a specific goal can be quickly identified. With this book close at hand, a quality improvement team becomes capable of more efficient and effective work with less assistance from a trained quality consultant. Quality and training professionals also will find it a handy reference and quick way to expand their repertoire of tools, techniques, applications, and tricks. For this second edition, Tague added 34 tools and 18 variations. The "Quality Improvement Stories" chapter has been expanded to include detailed case studies from three Baldrige Award winners. An entirely new chapter, "Mega-Tools: Quality Management Systems," puts the tools into two contexts: the historical evolution of quality improvement and the quality management systems within which the tools are used. This edition liberally uses icons with each tool description to reinforce for the reader what kind of tool it is and where it is used within the improvement process.

The SAGE Handbook of Qualitative Data Collection is a timely overview of the methodological developments available to social science researchers, covering key themes including: Concepts, Contexts, Basics Verbal Data Digital and Internet Data Triangulation and Mixed Methods Collecting Data in Specific Populations.

A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Prevention is better than cure. That’s as true about data collection as it is about anything else. Data collection might be an incredibly simple (and boring) task, but it’s also very easy to get wrong. Get it right and all the steps that follow, like data cleaning and preparation, analysis, plotting, statistics and machine learning, all fall into place and everything goes like a dream. But get it wrong and you’ve got weeks of pain ahead of you, correcting all the mistakes you made right at the beginning. Having a good data collection plan is the difference between a project that inspires you to go to work with a smile on your face every day and one where you struggle to drag yourself out of bed every morning. Your sanity is on the line here! Building a good data collection plan, believe it or not, is a roadmap to doing your entire study properly. Data Collection is a short guide to getting that plan up and running. It is a snappy little non-threatening book about everything you ever wanted to know (but were afraid to ask) about data collection, answers the most frequently asked questions and inspires you to take the next steps in your journey. First, I’ll explain why data is so important, and break down the differences between data, information and knowledge. Then we’ll look at why we collect data in the first place. I’ll explain the purpose of building a data collection plan and teach you about the 5 methods of data collection. Then I’ll give you my top tips for practical data collection to give you the best start possible. By the time you’ve read this short book, you’ll be able to create your own data collection plan that enables you to collect a dataset that is useful, accurate and fit-for-purpose – everything you need for a great research project. Data Collection makes no assumptions about your previous experience and is perfect for beginners and those just getting started with collecting data. Discover the world of Data Collection. Get this book, TODAY!