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Statistical methods and the improvement of data quality. (The Proceedings of the Small Conference on the Improvement of the Quality of Data Collected by Data Collection Systems, November 11-12, 1982, Oak Ridge, Tennessee). (English) Zbl 0601.62004

Orlando etc.: Academic Press, Inc. (Harcourt Brace Jovanovich, Publishers) XVII, 357 p.; $ 27.00 (1983).
From the preface: In recent years, the number of active large data systems has increased sharply with the demands for more information from decision makers. While recent observations suggest that this number may not be increasing for the moment, the need to improve the quality of collected data still exists. This need cuts across many fields, including agriculture, defense, economics, education, energy, environment, finance, health, labor, natural resources, population, and transportation. Practical experience in dealing with different types of data shows that they can contain gross errors. These errors, if left unchanged, directly affect analyses and decisions based on the data by leading to faulty decisions, inappropriate actions, and a loss of confidence in the data collection system.
This volume contains most of the papers presented at the conference. They are ordered approximately as they were presented during the conference. The first 11 papers are by invited speakers; the remaining 5 were contributed. The papers in this volume are likely to be of primary benefit to individuals and groups throughout the world who deal with large data collection systems and who are constantly seeking ways to improve the overall quality of their data. The papers can also be used to complement the material presented in a general course in survey sampling techniques.
Contents: T. Dalenius, Errors and other limitations of surveys (1- 24); T. Wright and H. J. Tsao, A frame on frames: An annotated bibliography (25-72); L. Kish, Data collection for details over space and time (73-84); S. Sudman, Response effects to behavior and attitude questions (85-116); B. A. Bailar, Error profiles: Uses and abuses (117-130); V. Barnett, Principles and methods for handling outliers in data sets (131-166); M. R. Chernick, Influence functions, outlier detection, and data editing (167- 176); P. F. Velleman and D. F. Williamson, Using exploratory data analysis to monitor socio-economic data quality in developing countries (177-192); R. C. Gonzalez, Application of pattern recognition techniques to data analysis (193-204); G. Liepins, Can automatic data editing be justified? One person’s opinion (205-214); R. J. A. Little and D. B. Rubin, Missing data in large data sets (215-244); J. T. Lessler and R. A. Kulka, Reducing the cost of studying survey measurement error: Is a laboratory approach the answer? (245-266); R. L. Williams, R. E. Folsom, and L. Morissey LaVange, The implication of sample design on survey data analysis (267-296); A. R. Silverberg, An approach to an evaluation of the quality of motor gasoline prices (297-320); K. C. Gissel, M. L. Wray, and M. S. Hansard, Health and mortality study error detection, reporting, and resolution system (321-332); T. A. Curran III and R. D. Small, On using exploratory data analysis as an aid in modelling and statistical forecasting (333-354). Index

MSC:

62-07 Data analysis (statistics) (MSC2010)
62-02 Research exposition (monographs, survey articles) pertaining to statistics
62-06 Proceedings, conferences, collections, etc. pertaining to statistics
62D05 Sampling theory, sample surveys