Statistical Analysis with Missing Data Roderick J. A. Little and Donald B. Rubin Acknowledged experts on the subject bring together diverse sources on methods for statistical analysis of data sets with missing values, a pervasive problem for which standard methods are of limited value. Blending theory and application, it reviews historical approaches to the subject, and rigorous yet simple methods for multivariate analysis with missing values. Goes on to provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism. 1987 0 471-80254-9 275 pp. Multiple Imputation for Nonresponse in Surveys Donald B. Rubin Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing in handling such surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modem statistics. 1987 0 471-08705-X 250 pp. Measurement Error Models Wayne A. Fuller The first complete treatment of the theory and applications for measurement error problems (measurement of errors in collected data). Recent results for nonlinear models and for models with unequal variances are presented, the estimation of true values for the fixed models and prediction of true values under the random model are discussed, and models checks and the analysis of residuals are treated. Includes real-data examples along with twenty different data sets. 1987 0 471-86187-1 450 pp
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About the author Leslie Kish is Professor Emeritus at the University of Michigan where he was a founder (1947) of the Institute for Social Research and selected as Henry Russel Lecturer for 1981. He is the author of Survey Sampling (Wiley, 1965) and of many articles. Dr. Kish was President of the American Statistical Association (1977), and of the International Association of Survey Statisticians (1983?85). He is an Honorary Fellow of the Royal Statistical Society and a member of the International Statistical Institute.
Even before statistical data are collected for research, essential decisions of design must be made, decisions which will have significant effects on the validity and efficiency of studies. Yet virtually the entire literature on statistics addresses concerns of analysis?and glosses over the crucial issues of collection and selection, issues that go to the heart of the methods and philosophy of the field. Statistical Design for Research delves into the criteria and decisions that underlie accurate, valid methodology. It pursues the uses and ramifications of three distinct design methods: experimental designs, survey sampling, and controlled investigations. Stressing the importance of the basic choice among these design strategies, the treatment explicates the analytical uses of sample surveys; designs for comparisons; controls for disturbing variables; samples and censuses; sample designs over time; and several distinct problems over time. Throughout, coverage is broad enough to encompass the common considerations in many related fields, including the social sciences, health sciences, education, and market research. It can serve as a textbook for university sources and as a source book for research workers. The text is enhanced by 37 tables and figures, over 50 problems, and a comprehensive list of references, notated to indicate the section where each is cited in the text. By claiming for statistics the usually neglected primary aspects of research design, Statistical Design for Research will produce more accurate, practical, and economical designs for future statistical analysis.
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