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The authoritative reference on nonparametric methods for evaluating longitudinal data in factorial designs Broadening the range of techniques that can be used to evaluate longitudinal data, Nonparametric Analysis of Longitudinal Data in Factorial Experiments presents nonparametric methods of evaluation that supplement the generalized linear models approach. Emphasizing the practical application of these methods in statistical procedures, this book provides a unified approach for the analysis of factorial designs involving longitudinal data that is appropriate for metric data, count data, ordered categorical data, and dichotomous data. Topics covered include nonparametric models, effects and hypotheses in experimental design, estimators for relative effects, experiments for one and several groups of subjects, multifactorial experiments, dependent replications, and experiments with numerous time points. The basic mathematical principles for the methods introduced here are described in theory, consistent with the book's minimal math requirements. Simple approximations for small data sets are provided, as well as ample chapter exercises to test skills, an appendix that includes original data for the examples used throughout the book, and downloadable SAS-IML macros for implementing the more extensive calculations. All applications are designed to be useful in many fields. Generously supplemented with more than 110 graphs and tables, Nonparametric Analysis of Longitudinal Data in Factorial Experiments is an essential reference for statisticians and biometricians, researchers in clinical trials, psychological studies, and in the fields of forestry, agriculture, sociology, ecology, and biology, as well as graduate students in statistics and biostatistics.

The authoritative reference on nonparametric methods for evaluating longitudinal data in factorial designs Broadening the range of techniques that can be used to evaluate longitudinal data, Nonparametric Analysis of Longitudinal Data in Factorial Experiments presents nonparametric methods of evaluation that supplement the generalized linear models approach. Emphasizing the practical application of these methods in statistical procedures, this book provides a unified approach for the analysis of factorial designs involving longitudinal data that is appropriate for metric data, count data, ordered categorical data, and dichotomous data. Topics covered include nonparametric models, effects and hypotheses in experimental design, estimators for relative effects, experiments for one and several groups of subjects, multifactorial experiments, dependent replications, and experiments with numerous time points. The basic mathematical principles for the methods introduced here are described in theory, consistent with the book's minimal math requirements. Simple approximations for small data sets are provided, as well as ample chapter exercises to test skills, an appendix that includes original data for the examples used throughout the book, and downloadable SAS-IML macros for implementing the more extensive calculations. All applications are designed to be useful in many fields. Generously supplemented with more than 110 graphs and tables, Nonparametric Analysis of Longitudinal Data in Factorial Experiments is an essential reference for statisticians and biometricians, researchers in clinical trials, psychological studies, and in the fields of forestry, agriculture, sociology, ecology, and biology, as well as graduate students in statistics and biostatistics.

An innovative discussion of building empirical models and the fitting of surfaces to data. Introduces the general philosophy of response surface methodology, and details least squares for response surface work, factorial designs at two levels, fitting second-order models, adequacy of estimation and the use of transformation, occurrence and elucidation of ridge systems, and more. Some results are presented for the first time. Includes real-life exercises, nearly all with solutions.

Research Designs is a clear, compact introduction to the principles of experimental and non-experimental design especially written for social scientists and their students. Spector covers major designs including: single group designs; pre-test/post-test designs; factorial designs, hierarchical designs; multivariate designs; the Solomon four group design; panel designs; and designs with concomitant variables.

"Superbly demonstrating the conception of factorials in mathematics, the text and pictures combine to present a lesson in a palatable form as well as the pleasure of looking at pictures that only Mitsumasa Anno could invent." — "Publishers Weekly "The book moves logically and elegantly from the concrete to the abstract. Once made clear, the concept is unforgettable, but the book entices the reader to return for further appreciation of the artistry with which it was designed." — "The Horn Book" "If you want a book that does a good selling job on the factorial concept, this one works." — "School Library Journal"

Completely revised and expanded, this Third Edition maintains a balance between design and analysis topics, but includes new material and numerous updated examples. Added, expanded and reorganized chapters provide more in-depth treatment of 2k factorial and fractional factorial designs; brand new topical coverage of factorial and fractional factorial design; a discussion of the Taguchi approach to parameter design, along with a critique of this method and recommended alternatives; additional topics on the selection of a response surface design; and an introduction to the mixture problem. Throughout, it stresses the importance of experimental design (as a tool for the practicing engineer) to product design and development, process development and improvement and process troubleshooting.

Research Designs is a clear, compact introduction to the principles of experimental and non-experimental design especially written for social scientists and their students. Spector covers major designs including: single group designs; pre-test/post-test designs; factorial designs, hierarchical designs; multivariate designs; the Solomon four group design; panel designs; and designs with concomitant variables.

Simple text and pictures introduce the mathematical concept of factorials.

"Superbly demonstrating the conception of factorials in mathematics, the text and pictures combine to present a lesson in a palatable form as well as the pleasure of looking at pictures that only Mitsumasa Anno could invent." — Publishers Weekly"The book moves logically and elegantly from the concrete to the abstract. Once made clear, the concept is unforgettable, but the book entices the reader to return for further appreciation of the artistry with which it was designed." — The Horn Book"If you want a book that does a good selling job on the factorial concept, this one works." — School Library Journal