Electronic data

About: Electronic data is a research topic. Over the lifetime, 10276 publications have been published within this topic receiving 177124 citations.

Machine Learning : A Probabilistic Perspective

Abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

The Algorithmic Foundations of Differential Privacy

Abstract: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition.After motivating and discussing the meaning of differential privacy, the preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some astonishingly powerful computational results, there are still fundamental limitations — not just on what can be achieved with differential privacy but on what can be achieved with any method that protects against a complete breakdown in privacy. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power. Certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed.We then turn from fundamentals to applications other than queryrelease, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams is discussed.Finally, we note that this work is meant as a thorough introduction to the problems and techniques of differential privacy, but is not intended to be an exhaustive survey — there is by now a vast amount of work in differential privacy, and we can cover only a small portion of it.

Research Methods in the Social Sciences

Abstract: This acclaimed text offers a comprehensive, systematic treatment of the scientific approach to research within the context of the social sciences. Emphasizing the relationship between theory, research, and practice, the book leads students through seven major, interrelated stages of research methods: definition of the research problem, statement of hypothesis, research design, measurement, data collection, data analysis, and generalization. Research activities are integrated throughout to get students actively involved in the real work of social science research. With its self-contained yet integrated chapters, the text adapts well to either a basic methods course, or a course that covers methods and statistics sequentially. The new edition has been thoroughly updated and revised, and is designed to help students take full advantage of the Internet and other electronic data sources plus the most current statistical software.

Survey response rate levels and trends in organizational research

Abstract: This study examines the response rates for surveys used in organizational research. We analysed 1607 studies published in the years 2000 and 2005 in 17 refereed academic journals, and we identified 490 different studies that utilized surveys. We examined the response rates in these studies, which covered more than 100,000 organizations and 400,000 individual respondents. The average response rate for studies that utilized data collected from individuals was 52.7 percent with a standard deviation of 20.4, while the average response rate for studies that utilized data collected from organizations was 35.7 percent with a standard deviation of 18.8. Key insights from further analysis include relative stability in response rates in the past decade and higher response rates for journals published in the USA. The use of incentives was not found to be related to response rates and, for studies of organizations, the use of reminders was associated with lower response rates. Also, electronic data collection efforts (e.g. email, phone, web) resulted in response rates as high as or higher than traditional mail methodology. We discuss a number of implications and recommendations.

Associations of Depression With C-Reactive Protein, IL-1, and IL-6 : A Meta-Analysis

Abstract: Objective:To assess the magnitude and direction of associations of depression with C-reactive protein (CRP), interleukin (IL)-1, and IL-6 in community and clinical samples.Methods:Systematic review of articles published between January 1967 and January 2008 in the PubMed and PsycINFO electronic data