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We live in the Information Age, with billions of bytes of data just two swipes away. Yet how much of this is mis- or even disinformation? A lot of it is, and your search engine can't tell the difference. As a result, an avalanche of misinformation threatens to overwhelm the discourse we so desperately need to address complex social problems such as climate change, the food and water crises, biodiversity collapse, and emerging threats to public health. This book provides an inoculation against the misinformation epidemic by cultivating scientific habits of mind. Anyone can do it—indeed, everyone must do it if our species is to survive on this crowded and finite planet.This survival guide supplies an essential set of apps for the prefrontal cortex while making science both accessible and entertaining. It will dissolve your fear of numbers, demystify graphs, and elucidate the key concepts of probability, all while celebrating the precise use of language and logic. David Helfand, one of our nation's leading astronomers and science educators, has taught scientific habits of mind to generations in the classroom, where he continues to wage a provocative battle against sloppy thinking and the encroachment of misinformation.

Errors, Scientific. --- Science --- Statistics --- Missing observations (Statistics) --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Scientific method --- Logic, Symbolic and mathematical --- Mistakes, Scientific --- Scientific errors --- Errors --- Methodology.

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Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences.  Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking.  The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power. Missing Data: Analysis and Design contains essential information for both beginners and advanced readers.  For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data; provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses; and offers practical advice, based on over 20 years' experience, for avoiding and troubleshooting problems.  For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided. The author lays out missing data theory in a plain English style that is accessible and precise.  Most analyses described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities.  A related web site contains free downloads of the supplementary software, as well as sample empirical data sets and a variety of practical exercises described in the book to enhance and reinforce the reader’s learning experience.  Missing Data: Analysis and Design and its web site work together to enable beginners to gain confidence in their ability to conduct missing data analysis, and more advanced readers to expand their skill set.  JOHN W. GRAHAM, PhD, is Professor of Biobehavioral Health at The Pennsylvania State University.  His research and publishing focus on the evaluation of health promotion and disease prevention interventions.  He specializes in evaluation research methods, including missing data analysis and design, structural equation modeling, and measurement.

Missing observations (Statistics). --- Missing observations (Statistics) --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Statistics. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Statistics, general. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics for Social Science, Behavioral Science, Education, Public Policy, and Law. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Statistics for Social Sciences, Humanities, Law. --- Statistics .

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This book develops methods for two key problems in the analysis of large-scale surveys: dealing with incomplete data and making inferences about sparsely represented subdomains. The presentation is committed to two particular methods, multiple imputation for missing data and multivariate composition for small-area estimation. The methods are presented as developments of established approaches by attending to their deficiencies. Thus the change to more efficient methods can be gradual, sensitive to the management priorities in large research organisations and multidisciplinary teams and to other reasons for inertia. The typical setting of each problem is addressed first, and then the constituency of the applications is widened to reinforce the view that the general method is essential for modern survey analysis. The general tone of the book is not "from theory to practice," but "from current practice to better practice." The third part of the book, a single chapter, presents a method for efficient estimation under model uncertainty. It is inspired by the solution for small-area estimation and is an example of "from good practice to better theory." A strength of the presentation is chapters of case studies, one for each problem. Whenever possible, turning to examples and illustrations is preferred to the theoretical argument. The book is suitable for graduate students and researchers who are acquainted with the fundamentals of sampling theory and have a good grounding in statistical computing, or in conjunction with an intensive period of learning and establishing one's own a modern computing and graphical environment that would serve the reader for most of the analytical work in the future. While some analysts might regard data imperfections and deficiencies, such as nonresponse and limited sample size, as someone else's failure that bars effective and valid analysis, this book presents them as respectable analytical and inferential challenges, opportunities to harness the computing power into service of high-quality socially relevant statistics. Overriding in this approach is the general principle—to do the best, for the consumer of statistical information, that can be done with what is available. The reputation that government statistics is a rigid procedure-based and operation-centred activity, distant from the mainstream of statistical theory and practice, is refuted most resolutely. After leaving De Montfort University in 2004 where he was a Senior Research Fellow in Statistics, Nick Longford founded the statistical research and consulting company SNTL in Leicester, England. He was awarded the first Campion Fellowship (2000–02) for methodological research in United Kingdom government statistics. He has served as Associate Editor of the Journal of the Royal Statistical Society, Series A, and the Journal of Educational and Behavioral Statistics and as an Editor of the Journal of Multivariate Analysis. He is a member of the Editorial Board of the British Journal of Mathematical and Statistical Psychology. He is the author of two other monographs, Random Coefficient Models (Oxford University Press, 1993) and Models for Uncertainty in Educational Testing (Springer-Verlag, 1995).

Surveys --- Social surveys --- Missing observations (Statistics) --- Estimation theory. --- Social sciences --- Methodology. --- Research --- Statistical methods. --- Estimating techniques --- Least squares --- Mathematical statistics --- Stochastic processes --- Data, Missing (Statistics) --- Missing data (Statistics) --- Missing values (Statistics) --- Observations, Missing (Statistics) --- Values, Missing (Statistics) --- Estimation theory --- Multivariate analysis --- Multiple imputation (Statistics) --- Government surveys --- Mathematical geography --- Mathematical statistics. --- Statistics. --- Computer simulation. --- Epidemiology. --- Psychometrics. --- Statistical Theory and Methods. --- Statistics for Social Sciences, Humanities, Law. --- Simulation and Modeling. --- Statistics and Computing/Statistics Programs. --- Diseases --- Public health --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Measurement --- Scaling --- Methodology --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Research&delete& --- Statistics .