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Statistical mathematics --- Experimental design --- Statistique mathématique --- Plan d'expérience --- experimentation. --- experimentation --- Statistical methods --- Agriculture --- Plan d'expérience --- Mathematical models --- Méthodes statistiques
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This book is about the statistical principles behind the design of effective experiments and focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis. Emphasising the logical principles of statistical design, rather than mathematical calculation, the authors demonstrate how all available information can be used to extract the clearest answers to many questions. The principles are illustrated with a wide range of examples drawn from real experiments in medicine, industry, agriculture and many experimental disciplines. Numerous exercises are given to help the reader practise techniques and to appreciate the difference that good design can make to an experimental research project. Based on Roger Mead's excellent Design of Experiments, this new edition is thoroughly revised and updated to include modern methods relevant to applications in industry, engineering and modern biology. It also contains seven new chapters on contemporary topics, including restricted randomisation and fractional replication.
Mathematical statistics --- Statistics --- Experimental design. --- Statistique --- Plan d'expérience --- Experimental design --- Mathematics --- Plan d'expérience --- Statistical decision. --- Decision problems --- Game theory --- Operations research --- Management science --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Analysis of means --- Analysis of variance --- Experiments --- Methodology --- Research design
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"Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. A variety of measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, And S-Plus are provided for the more tedious calculations. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, education, and other social sciences. Special attention is paid to confidence intervals, the statistical assumptions of the methods, and robust estimators of effect sizes. The extensive reference section is appreciated by all. With more than 40% new material, highlights of the new edition include: Three new multivariate chapters covering effect sizes for analysis of covariance, multiple regression/correlation, and multivariate analysis of variance. More learning tools in each chapter including introductions, summaries, "Tips and Pitfalls" and more conceptual and computational questions. More coverage of univariate effect sizes, confidence intervals, and effect sizes for repeated measures to reflect their increased use in research. More software references for calculating effect sizes and their confidence intervals including SPSS, SAS, R, and S-Plus. The data used in the book is now provided on the web along with suggested calculations for computational practice. Effect Sizes for Research, 2nd Edition covers standardized and unstandardized differences between means, correlational measures, strength of association, and parametric and nonparametric measures for between- and within-groups data. The book clearly demonstrates how the choice of an appropriate measure depends on such factors as whether variables are categorical, ordinal, or continuous; satisfying assumptions; sampling; and the source of variability in the population. Background information on multivariate statistics is provided for those who need it. Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is also an excellent supplement for advanced statistics courses in psychology, education, the social sciences, business, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended"--
Analysis of variance --- Effect sizes (Statistics) --- Experimental design --- Analyse de variance --- Plan d'expérience --- Effect sizes --- Univariate applications --- Multivariate applications --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- ANOVA (Analysis of variance) --- Variance analysis --- Mathematical statistics --- Experiments --- Methodology --- Analyse de variance. --- Plan d'expérience. --- Analysis of variance. --- EDUCATION / Statistics. --- Effect sizes (Statistics). --- Experimental design. --- PSYCHOLOGY / Statistics. --- SOCIAL SCIENCE / Statistics. --- Plan d'expérience.
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This unique book is the first comprehensive guide to the discovery, analysis, and evaluation of natural experiments - an increasingly popular methodology in the social sciences. Thad Dunning provides an introduction to key issues in causal inference, including model specification, and emphasizes the importance of strong research design over complex statistical analysis. Surveying many examples of standard natural experiments, regression-discontinuity designs, and instrumental-variables designs, Dunning highlights both the strengths and potential weaknesses of these methods, aiding researchers in better harnessing the promise of natural experiments while avoiding the pitfalls. Dunning also demonstrates the contribution of qualitative methods to natural experiments and proposes new ways to integrate qualitative and quantitative techniques. Chapters complete with exercises and appendices covering specialized topics such as cluster-randomized natural experiments, make this an ideal teaching tool as well as a valuable book for professional researchers.
Social sciences --- Experimental design --- Experiments --- Research --- Experiments. --- Expériences --- Plan d'expérience --- Expériences --- Methods in social research (general) --- Experimental design. --- Sciences sociales --- Research. --- Recherche --- Experiment. --- Sozialwissenschaften. --- Social science research --- Design of experiments --- Statistical design --- Mathematical optimization --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Methodology --- Science research --- Scientific research --- Information services --- Learning and scholarship --- Research teams --- Design. --- Methodology. --- Social Sciences --- Political Science --- Social sciences - Experiments --- Social sciences - Research
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