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Informationen zum Autor Michael Smithson is a Professor in the Research School of Psychology at The Australian National University in Canberra, and received his PhD from the University of Oregon. He is the author of Confidence Intervals (2003), Statistics with Confidence (2000), Ignorance and Uncertainty (1989), and Fuzzy Set Analysis for the Behavioral and Social Sciences (1987), co-author of Fuzzy Set Theory: Applications in the Social Sciences (2006) and Generalized Linear Models for Categorical and Limited Dependent Variables (2014), and co-editor of Uncertainty and Risk: Multidisciplinary Perspectives (2008) and Resolving Social Dilemmas: Dynamic, Structural, and Intergroup Aspects (1999). His other publications include more than 170 refereed journal articles and book chapters. His primary research interests are in judgment and decision making under ignorance and uncertainty, statistical methods for the social sciences, and applications of fuzzy set theory to the social sciences. Klappentext Using lots of easy to understand examples from different disciplines, the author introduces the basis of the confidence interval framework and provides the criteria for `best' confidence intervals, along with the trade-offs between confidence and precision. The book covers such pertinent topics as: the transformation principle whereby a confidence interval for a parameter may be used to construct an interval for any monotonic transformation of that parameter; confidence intervals on distributions whose shape changes with the value of the parameter being estimated; and, the relationship between confidence interval and significance testing frameworks, particularly regarding power. Zusammenfassung Introduces the basis of the confidence interval framework and provides the criteria for 'best' confidence intervals! along with the trade-offs between confidence and precision. This book covers topics such as the transformation principle! confidence intervals! and the relationship between confidence interval and significance testing frameworks. Inhaltsverzeichnis Ch 1 Introduction and Overview Ch 2 Confidence Statements and Interval Estimates Why Confidence Intervals? Ch 3 Central Confidence Intervals Central and Standardizable versus Noncentral Distributions Confidence Intervals Using the Central t and Normal Distributions Confidence Intervals Using the Central Chi-Square and F Distributions Transformation Principle Ch 4 Noncentral Confidence Intervals for Standardized Effect Sizes Noncentral Distributions Computing Noncentral Confidence Intervals Ch 5 Applications in Anova and Regression Fixed-Effects ANOVA Random-Effects ANOVA A Priori and Post-Hoc Contrasts Regression: Multiple, Partial, and Semi-Partial Correlations Effect-Size Statistics for MANOVA and Setwise Regression Confidence Interval for a Regression Coefficient Goodness of Fit Indices in Structural Equations Models Ch 6 Applications in Categorical Data Analysis Odds Ratio, Difference between Proportions and Relative Risk Chi-Square Confidence Intervals for One Variable Two-Way Contingency Tables Effects in Log-Linear and Logistic Regression Models Ch 7 Significance Tests and Power Analysis Significance Tests and Model Comparison Power and Precision Designing Studies Using Power Analysis and Confidence Intervals Confidence Intervals for Power Concluding Remarks References About the Author ...
