Data Quality
Data Quality

Author: Carlo Batini

Publisher: Springer Science & Business Media

Published: 2006-09-27

Total Pages: 276

ISBN-13: 3540331735

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Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.

Key Concepts in Social Research
Key Concepts in Social Research

Author: Geoff Payne

Publisher: SAGE

Published: 2004-03-18

Total Pages: 249

ISBN-13: 1848600623

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`This clearly written and user-friendly book is ideal for students or researchers who wish to get a basic, but solid grasp of a topic and see how it fits with other topics. By following the links a student can easily and efficiently build up a clear conceptual map of social research′ - Malcolm Williams, Reader in Sociology, Cardiff University `This is a really useful book, written in an accessible manner for students beginning their study of social research methods. It is helpful both as an introductory text and as a reference guide for more advanced students. Most of the key topics in methods and methodology are covered and it will be suitable as a recommended text on a wide variety of courses′ - Clive Seale, Brunel University At last, an authoritative, crystal-clear introduction to research methods which really takes account of the needs of students for accessible, focused information to help with undergraduate essays and exams. The key concepts discussed here are based on a review of teaching syllabi and the authors′ experience of many years of teaching. Topics range over qualitative and quantitative approaches and combine practical considerations with philosophical issues. They include several new topics, like internet and phone polling, internet searches, and visual methods. Each section is free-standing, can be tackled in order, but with links to other sections to enable students to cross-reference and build up a wider understanding of central research methods. To facilitate comprehension and aid study, each section begins with a definition. It is followed by a summary of key points with key words and guides to further reading and up-to-date examples. The book is a major addition to undergraduate reading lists. It is reliable, allows for easy transference to essays and exams and easy to use, and exceptionally clearly written for student consumption. The book answers the needs of all those who find research methods daunting, and for those who have dreamt of an ideal introduction to the subject.

Analyzing Quantitative Data
Analyzing Quantitative Data

Author: Norman Blaikie

Publisher: SAGE

Published: 2003-03-06

Total Pages: 380

ISBN-13: 9780761967590

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For social researchers who need to know what procedures to use under what circumstances in practical research projects, this book does not require an indepth understanding of statistical theory.

Encyclopedia of Research Design
Encyclopedia of Research Design

Author: Neil J. Salkind

Publisher: SAGE

Published: 2010-06-22

Total Pages: 1779

ISBN-13: 1412961270

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"Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases."--Publisher's description.

Core Concepts in Data Analysis: Summarization, Correlation and Visualization
Core Concepts in Data Analysis: Summarization, Correlation and Visualization

Author: Boris Mirkin

Publisher: Springer Science & Business Media

Published: 2011-04-05

Total Pages: 402

ISBN-13: 0857292870

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Core Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule). Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval. Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data. The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.

Sharing Clinical Trial Data
Sharing Clinical Trial Data

Author: Institute of Medicine

Publisher: National Academies Press

Published: 2015-04-20

Total Pages: 236

ISBN-13: 0309316324

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Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.

Social Science Research
Social Science Research

Author: Anol Bhattacherjee

Publisher: CreateSpace

Published: 2012-04-01

Total Pages: 156

ISBN-13: 9781475146127

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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.