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Author:   Richard Gonzalez ,  Michael Milburn ,  Rick H. Hoyle ,  John R Nesselroade
Publisher:   Guilford Publications
Imprint:   Guilford Publications
Dimensions:   Width: 17.80cm , Height: 2.70cm , Length: 25.40cm
Weight:   0.962kg
ISBN:  

9781606230176

ISBN 10:   1606230174
Pages:   439
Publication Date:   31 October 2008
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Active
Availability:   In stock  
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Table of Contents

_x000D_ _x000D_ 1. The Nature of Research _x000D_ 1.1 Introduction _x000D_ 1.2 Observations and Variables _x000D_ 1.3 Behavioral Variables _x000D_ 1.4 Stimulus Variables _x000D_ 1.5 Individual Difference Variables _x000D_ 1.6 Discrete and Continuous Variables _x000D_ 1.7 Levels of Measurement _x000D_ 1.8 Summarizing Observations in Research _x000D_ 1.9 Questions and Problems _x000D_ 2. Principles of Experimental Design _x000D_ 2.1 The Farmer from Whidbey Island _x000D_ 2.2 The Experiment _x000D_ 2.3 The Question of Interest _x000D_ 2.4 Sample Space and Probability _x000D_ 2.5 Simulation of the Experiment _x000D_ 2.6 Permutations _x000D_ 2.7 Combinations _x000D_ 2.8 Probabilities of Possible Outcomes _x000D_ 2.9 A Sample Space for the Experiment _x000D_ 2.10 Testing a Null Hypothesis _x000D_ 2.11 Type I and Type II Errors _x000D_ 2.12 Experimental Controls _x000D_ 2.13 The Importance of Randomization _x000D_ 2.14 A Variation in Design _x000D_ 2.15 Summary _x000D_ 2.16 Questions and Problems _x000D_ 3. The Standard Normal Distribution: An Amazing Approximation _x000D_ 3.1 Introduction _x000D_ 3.2 Binomial Populations and Binomial Variables _x000D_ 3.3 Mean of a Population _x000D_ 3.4 Variance and Standard Deviation of a Population _x000D_ 3.5 The Average of a Sum and the Variance of a Sum _x000D_ 3.6 The Average and Variance of Repeated Samples _x000D_ 3.7 The Second Experiment with the Farmer: T and sT _x000D_ 3.8 Representing Probabilities by Areas _x000D_ 3.9 The Standard Normal Distribution _x000D_ 3.10 The Second Experiment with the Farmer: A Normal Distribution Test _x000D_ 3.11 The First Experiment with the Farmer: A Normal Distribution Test _x000D_ 3.12 Examples of Binomial Models _x000D_ 3.13 Populations That Have Several Possible Values _x000D_ 3.14 The Distribution of the Sum from a Uniform Distribution _x000D_ 3.15 The Distribution of the Sum T from a U-Shaped Population _x000D_ 3.16 The Distribution of the Sum T from a Skewed Population _x000D_ 3.17 Summary and Sermon _x000D_ 3.18 Questions and Problems _x000D_ 4. Tests for Means from Random Samples _x000D_ 4.1 Transforming a Sample Mean into a Standard Normal Variable _x000D_ 4.2 The Variance and Standard Error of the Mean When the Population Variance s2 Is Known _x000D_ 4.3 The Variance and Standard Error of the Mean When Population s2 Is Unknown _x000D_ 4.4 The t Distribution and the One-Sample t Test _x000D_ 4.5 Confidence Interval for a Mean _x000D_ 4.6 Standard Error of the Difference between Two Means _x000D_ 4.7 Confidence Interval for a Difference between Two Means _x000D_ 4.8 Test of Significance for a Difference between Two Means: The Two-Sample t Test _x000D_ 4.9 Using a Computer Program _x000D_ 4.10 Returning to the Farmer Example in Chapter 2 _x000D_ 4.11 Effect Size for a Difference between Two Independent Means _x000D_ 4.12 The Null Hypothesis and Alternatives _x000D_ 4.13 The Power of the t Test against a Specified Alternative _x000D_ 4.14 Estimating the Number of Observations Needed in Comparing Two Treatment Means _x000D_ 4.15 Random Assignments of Participants _x000D_ 4.16 Attrition in Behavioral Science Experiments _x000D_ 4.17 Summary _x000D_ 4.18 Questions and Problems _x000D_ 5. Homogeneity and Normality Assumptions _x000D_ 5.1 Introduction _x000D_ 5.2 Testing Two Variances: The F Distribution _x000D_ 5.3 An Example of Testing the Homogeneity of Two Variances _x000D_ 5.4 Caveats _x000D_ 5.5 Boxplots _x000D_ 5.6 A t Test for Two Independent Means When the Population Variances Are Not Equal _x000D_ 5.7 Nonrandom Assignment of Subjects _x000D_ 5.8 Treatments That Operate Differentially on Individual Difference Variables _x000D_ 5.9 Nonadditivity of a Treatment Effect _x000D_ 5.10 Transformations of Raw Data _x000D_ 5.11 Normality _x000D_ 5.12 Summary _x000D_ 5.13 Questions and Problems _x000D_ 6. The Analysis of Variance: One Between-Subjects Factor _x000D_ 6.1 Introduction _x000D_ 6.2 Notation for a One-Way Between-Subjects Design _x000D_ 6.3 Sums of Squares for the One-Way Between-Subjects Design _x000D_ 6.4 One-Way Between-Subjects Design: An Example _x000D_ 6.5 Test of Significance for a One-Way Between-Subjects Design _x000D_ 6.6 Weighted Means Analysis with Unequal n's _x000D_ 6.7 Summary _x000D_ 6.8 Questions and Problems _x000D_ 7. Pairwise Comparisons _x000D_ 7.1 Introduction _x000D_ 7.2 A One-Way Between-Subjects Experiment with 4 Treatments _x000D_ 7.3 Protection Levels and the Bonferroni Significant Difference (BSD) Test _x000D_ 7.4 Fisher's Significant Difference (FSD) Test _x000D_ 7.5 The Tukey Significant Difference (TSD) Test _x000D_ 7.6 Scheffe's Significant Difference (SSD) Test _x000D_ 7.7 The Four Methods: General Considerations _x000D_ 7.8 Questions and Problems _x000D_ 8. Orthogonal, Planned and Unplanned Comparisons _x000D_ 8.1 Introduction _x000D_ 8.2 Comparisons on Treatment Means _x000D_ 8.3 Standard Error of a Comparison _x000D_ 8.4 The t Test of Significance for a Comparison _x000D_ 8.5 Orthogonal Comparisons _x000D_ 8.6 Choosing a Set of Orthogonal Comparisons _x000D_ 8.7 Protection Levels with Orthogonal Comparisons _x000D_ 8.8 Treatments as Values of an Ordered Variable _x000D_ 8.9 Coefficients for Orthogonal Polynomials _x000D_ 8.10 Tests of Significance for Trend Comparisons _x000D_ 8.11 The Relation between a Set of Orthogonal Comparisons and the Treatment Sum of Squares _x000D_ 8.12 Tests of Significance for Planned Comparisons _x000D_ 8.13 Effect Size for Comparisons _x000D_ 8.14 The Equality of Variance Assumption _x000D_ 8.15 Unequal

Reviews

I could see using this book in an upper-level experimental methods course for undergraduates, or in a first course for graduate students in psychology, assuming they have all had introductory statistics. - Michael Milburn, Department of Psychology, University of Massachusetts, Boston The discussion of simple ANOVA concepts leads delightfully into more elaborate or general models. One of the very real strengths of this text is its treatment of multiple-comparison methods. There is a wonderful discussion of planned and unplanned contrasts and their use with or without preceding omnibus significance tests. The discussion of orthogonal contrasts and orthogonal polynomials is another strength. - Warren E. Lacefield, Department of Educational Leadership, Research, and Technology, Western Michigan University This book is up to date, clearly written, and has a well-crafted array of study questions and exercises at the end of each chapter that will benefit both instructors and students. The strong links to modern statistical software will be appreciated, as will the patient explanations regarding what one is really doing when analyzing data - and why. - John R. Nesselroade, Hugh Scott Hamilton Professor of Psychology, University of Virginia Data Analysis for Experimental Design goes beyond the standard factual presentation to offer insights on strategy and interpretation. Detailed and engaging, the book builds logically from a small set of principles involving design, sampling, distributions, and inference to offer a thorough treatment of tests of hypotheses involving means. The author uses clever and incisive examples to illustrate fundamental aspects of research design and strategy. Relatively little prior training in statistical methods is assumed, making this an excellent text for a first course in applied statistical methods for graduate students. - Rick H. Hoyle, Department of Psychology and Neuroscience, Duke University The book provides graduate students and behavioral science researchers with a thorough introduction to experimental design, with an emphasis on developing a simple and intuitive understanding of the basic concepts of analysis of variance. The strength of this book lies in the clear exposition of complex statistical ideas and the comprehensive coverage of the subject area. The book is also noteworthy for its special attention to proper interpretations of hypothesis-testing results, confidence intervals, and effect size, as well as for its explicit treatment of technical assumptions underlying statistical tests. This excellent text is highly recommended. - Jay Myung, Department of Psychology, Ohio State University

""I could see using this book in an upper-level experimental methods course for undergraduates, or in a first course for graduate students in psychology, assuming they have all had introductory statistics."" - Michael Milburn, Department of Psychology, University of Massachusetts, Boston ""The discussion of simple ANOVA concepts leads delightfully into more elaborate or general models. One of the very real strengths of this text is its treatment of multiple-comparison methods. There is a wonderful discussion of planned and unplanned contrasts and their use with or without preceding omnibus significance tests. The discussion of orthogonal contrasts and orthogonal polynomials is another strength."" - Warren E. Lacefield, Department of Educational Leadership, Research, and Technology, Western Michigan University ""This book is up to date, clearly written, and has a well-crafted array of study questions and exercises at the end of each chapter that will benefit both instructors and students. The strong links to modern statistical software will be appreciated, as will the patient explanations regarding what one is really doing when analyzing data - and why."" - John R. Nesselroade, Hugh Scott Hamilton Professor of Psychology, University of Virginia ""Data Analysis for Experimental Design goes beyond the standard factual presentation to offer insights on strategy and interpretation. Detailed and engaging, the book builds logically from a small set of principles involving design, sampling, distributions, and inference to offer a thorough treatment of tests of hypotheses involving means. The author uses clever and incisive examples to illustrate fundamental aspects of research design and strategy. Relatively little prior training in statistical methods is assumed, making this an excellent text for a first course in applied statistical methods for graduate students."" - Rick H. Hoyle, Department of Psychology and Neuroscience, Duke University ""The book provides graduate students and behavioral science researchers with a thorough introduction to experimental design, with an emphasis on developing a simple and intuitive understanding of the basic concepts of analysis of variance. The strength of this book lies in the clear exposition of complex statistical ideas and the comprehensive coverage of the subject area. The book is also noteworthy for its special attention to proper interpretations of hypothesis-testing results, confidence intervals, and effect size, as well as for its explicit treatment of technical assumptions underlying statistical tests. This excellent text is highly recommended."" - Jay Myung, Department of Psychology, Ohio State University

This book is up to date, clearly written, and has a well-crafted array of study questions and exercises at the end of each chapter that will benefit both instructors and students. The strong links to modern statistical software will be appreciated, as will the patient explanations regarding what one is really doing when analyzing data--and why. --John R. Nesselroade, PhD, Hugh Scott Hamilton Professor of Psychology, University of Virginia Data Analysis for Experimental Design goes beyond the standard factual presentation to offer insights on strategy and interpretation. Detailed and engaging, the book builds logically from a small set of principles involving design, sampling, distributions, and inference to offer a thorough treatment of tests of hypotheses involving means. The author uses clever and incisive examples to illustrate fundamental aspects of research design and strategy. Relatively little prior training in statistical methods is assumed, making this an excellent text for a first course in applied statistical methods for graduate students. --Rick H. Hoyle, PhD, Department of Psychology and Neuroscience, Duke University The book provides graduate students and behavioral science researchers with a thorough introduction to experimental design, with an emphasis on developing a simple and intuitive understanding of the basic concepts of analysis of variance. The strength of this book lies in the clear exposition of complex statistical ideas and the comprehensive coverage of the subject area. The book is also noteworthy for its special attention to proper interpretations of hypothesis-testing results, confidence interval, and effect size, as well as for its explicit treatment of technical assumptions underlying statistical tests. This excellent text is highly recommended. --Jay Myung, PhD, Department of Psychology, Ohio State University The discussion of simple ANOVA concepts leads delightfully into more elaborate or general models. One of the very real strengths of this text is its treatment of multiple-comparison methods. There is a wonderful discussion of planned and unplanned contrasts and their use with or without preceding omnibus significance tests. The discussion of orthogonal contrasts and orthogonal polynomials is another strength. --Warren E. Lacefield, PhD, Department of Educational Leadership, Research, and Technology, Western Michigan University The arrangement of topics, flow of discussion, conversational language, and general coverage make this a highly readable and informative textbook. Students and instructors will especially appreciate the author's 'storytelling' approach, which is interesting and relevant as well as conceptually rigorous. --Warren E. Lacefield, PhD, Department of Educational Leadership, Research, and Technology, Western Michigan University I could see using this book in an upper-level experimental methods course for undergraduates, or in a first course for graduate students in psychology, assuming they have all had introductory statistics. --Michael Milburn, PhD, Department of Psychology, University of Massachusetts-Boston

Author Information

Richard Gonzalez is Professor of Psychology at the University of Michigan. He also holds faculty appointments in the Department of Statistics at the University of Michigan and in the Department of Marketing at the Ross School of Business; is a Research Professor at the Research Center for Group Dynamics, which is housed in the Institute for Social Research, University of Michigan; and has taught statistics courses to social science students at all levels at the University of Washington, the University of Warsaw, the University of Michigan, and Princeton University. Dr. Gonzalez's research is in the area of judgment and decision making. His empirical and theoretical research deals with how people make decisions. Given that behavioral scientists make decisions from their data, his interest in decision processes automatically led Dr. Gonzalez to the study of statistical inference. His research contributions in data analysis include statistical methods for interdependent data, multidimensional scaling, and structural equations modeling. Dr. Gonzalez is currently Associate Editor of American Psychologist, and is on the editorial boards of Psychological Methods, Psychological Review, Psychological Science, and the Journal of Experimental Psychology: Learning, Memory, and Cognition. He is an elected member of the Society of Experimental Social Psychology and of the Society of Multivariate Experimental Psychology.

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