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Author:   Herve Abdi (University of Texas, Dallas, USA) ,  Betty Edelman (University of Texas, Dallas, USA) ,  Dominique Valentin (University of Bourgogne, Dijon, France) ,  W. Jay Dowling (University of Texas, Dallas, USA)
Publisher:   Oxford University Press
Imprint:   Oxford University Press
Dimensions:   Width: 19.50cm , Height: 2.90cm , Length: 26.50cm
Weight:   1.176kg
ISBN:  

9780199299881

ISBN 10:   0199299889
Pages:   560
Publication Date:   26 February 2009
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

"1 Introduction to Experimental Design 1.1: Introduction 1.2: Independent and dependent variables 1.3: Independent variables 1.4: Dependent variables 1.5: Choice of subjects and representative design of experiments 1.7: Key notions of the chapter 2 Correlation 2.1: Introduction 2.2: Correlation: Overview and Example 2.3: Rationale and computation of the coefficient of correlation 2.4: Interpreting correlation and scatterplots 2.5: The importance of scatterplots 2.6: Correlation and similarity of distributions 2.7: Correlation and Z-scores 2.8: Correlation and causality 2.9: Squared correlation as common variance 2.10: Key notions of the chapter 2.11: Key formulas of the chapter 2.12: Key questions of the chapter 3 Statistical Test: The F test 3.1: Introduction 3.2: Statistical Test 3.3: Not zero is not enough! 3.4: Key notions of the chapter 3.5: New notations 3.6: Key formulas of the chapter 3.7: Key questions of the chapter 4 Simple Linear Regression 4.1: Introduction 4.2: Generalities 4.3: The regression line is the ""best-fit"" line 4.4: Example: Reaction Time and Memory Set 4.5: How to evaluate the quality of prediction 4.6: Partitioning the total sum of squares 4.7: Mathematical Digressions 4.8: Key notions of the chapter 4.9: New notations 4.10: Key formulas of the chapter 4.11: Key questions of the chapter 5 Orthogonal Multiple Regression 5.1: Introduction 5.2: Generalities 5.3: The regression plane is the ""best-fit"" plane 5.4: Back to the example: Retroactive interference 5.5: How to evaluate the quality of the prediction 5.6: F tests for the simple coefficients of correlation 5.7: Partitioning the sums of squares 5.8: Mathematical Digressions 5.9: Key notions of the chapter 5.10: New notations 5.11: Key formulas of the chapter 5.12: Key questions of the chapter 6 Non-Orthogonal Multiple Regression 6.1: Introduction 6.2: Example: Age, speech rate and memory span 6.3: Computation of the regression plane 6.4: How to evaluate the quality of the prediction 6.5: Semi-partial correlation as increment in explanation 6.5: F tests for the semi-partial correlation coefficients 6.6: What to do with more than two independent variables 6.7: Bonus: Partial correlation 6.8: Key notions of the chapter 6.9: New notations 6.10: Key formulas of the chapter 6.11: Key questions of the chapter 7 ANOVA One Factor: Intuitive Approach and Computation of F 7.1: Introduction 7.2: Intuitive approach 7.3: Computation of the F ratio 7.4: A bit of computation: Mental Imagery 7.5: Key notions of the chapter 7.6: New notations 7.7: Key formulas of the chapter 7.8: Key questions of the chapter 8 ANOVA, One Factor: Test, Computation, and Effect Size 8.1: Introduction 8.2: Statistical test: A refresher 8.3: Example: back to mental imagery 8.4: Another more general notation: A and S(A) 8.5: Presentation of the ANOVA results 8.6: ANOVA with two groups: F and t 8.7: Another example: Romeo and Juliet 8.8: How to estimate the effect size 8.9: Computational formulas 8.10: Key notions of the chapter 8.11: New notations 8.12: Key formulas of the chapter 8.13: Key questions of the chapter 9 ANOVA, one factor: Regression Point of View 9.1: Introduction 9.2: Example 1: Memory and Imagery 9.3: Analysis of variance for Example 1 9.4: Regression approach for Example 1: Mental Imagery 9.5: Equivalence between regression and analysis of variance 9.6: Example 2: Romeo and Juliet 9.7: If regression and analysis of variance are one thing, why keep two different techniques? 9.8: Digression: when predicting Y from Ma., b=1 9.9: Multiple regression and analysis of variance 9.10: Key notions of the chapter 9.11: Key formulas of the chapter 9.12: Key questions of the chapter 10 ANOVE, one factor: Score Model 10.1: Introduction 10.2: ANOVA with one random factor (Model II) 10.3: The Score Model: Model II 10.4: F < 1 or The Strawberry Basket 10.5: Size effect coefficients derived from the score model: w2 and p2 10.6: Three exercises 10.7: Key notions of the chapter 10.8: New notations 10.9: Key formulas of the chapter 10.10: Key questions of the chapter 11 Assumptions of Analysis of Variance 11.1: Introduction 11.2: Validity assumptions 11.3: Testing the Homogeneity of variance assumption 11.4: Example 11.5: Testing Normality: Lilliefors 11.6: Notation 11.7: Numerical example 11.8: Numerical approximation 11.9: Transforming scores 11.10: Key notions of the chapter 11.11: New notations 11.12: Key formulas of the chapter 11.13: Key questions of the chapter 12 Analysis of Variance, one factor: Planned Orthogonal Comparisons 12.1: Introduction 12.2: What is a contrast? 12.3: The different meanings of alpha 12.4: An example: Context and Memory 12.5: Checking the independence of two contrasts 12.6: Computing the sum of squares for a contrast 12.7: Another view: Contrast analysis as regression 12.8: Critical values for the statistical index 12.9: Back to the Context 12.10: Significance of the omnibus F vs. significance of specific contrasts 12.11: How to present the results of orthogonal comparisons 12.12: The omnibus F is a mean 12.13: Sum of orthogonal contrasts: Subdesign analysis 12.14: Key notions of the chapter 12.15: New notations 12.16: Key formulas of the chapter 12.17: Key questions of the chapter 13 ANOVA, one factor: Planned Non-orthogonal Comparisons 13.1: Introduction 13.2: The classical approach 13.3: Multiple regression: The return! 13.4: Key notions of the chapter 13.5: New notations 13.6: Key formulas of the chapter 13.7: Key questions of the chapter 14 ANOVA, one factor: Post hoc or a posteriori analyses 14.1: Introduction 14.2: Scheffe's test: All possible contrasts 14.3: Pairwise comparisons 14.4: Key notions of the chapter 14.5: New notations 14.6: Key questions of the chapter 15 More on Experimental Design: Multi-Factorial Designs 15.1: Introduction 15.2: Notation of experimental designs 15.3: Writing down experimental designs 15.4: Basic experimental designs 15.5: Control factors and factors of interest 15.6: Key notions of the chapter 15.7: Key questions of the chapter 16 ANOVA, two factors: AxB or S(AxB) 16.1: Introduction 16.2: Organization of a two-factor design: AxB 16.3: Main effects and interaction 16.4: Partitioning the experimental sum of squares 16.5: Degrees of freedom and mean squares 16.6: The Score Model (Model I) and the sums of squares 16.7: An example: Cute Cued Recall 16.8: Score Model II: A and B random factors 16.9: ANOVA AxB (Model III): one factor fixed, one factor random 16.10: Index of effect size 16.11: Statistical assumptions and conditions of validity 16.12: Computational formulas 16.13: Relationship between the names of the sources of variability, df and SS 16.14: Key notions of the chapter 16.15: New notations 16.16: Key formulas of the chapter 16.17: Key questions of the chapter 17 Factorial designs and contrasts 17.1: Introduction 17.2: Vocabulary 17.3: Fine grained partition of the standard decomposition 17.4: Contrast analysis in lieu of the standard decomposition 17.5: What error term should be used? 17.6: Example: partitioning the standard decomposition 17.7: Example: a contrtast non-orthogonal to the canonical decomposition 17.8: A posteriori Comparisons 17.9: Key notions of the chapter 17.10: Key questions of the chapter 18 ANOVA, one factor Repeated Measures design: SxA 18.1: Introduction 18.2: Advantages of repeated measurement designs 18.3: Examination of the F Ratio 18.4: Partitioning the within-group variability: S(A) = S + SA 18.5: Computing F in an SxA design 18.6: Numerical example: SxA design 18.7: Score Model: Models I and II for repeated measures designs 18.8: Effect size: R, R and R 18.9: Problems with repeated measures 18.10: Score model (Model I) SxA design: A fixed 18.11: Score model (Model II) SxA design: A random 18.12: A new assumption: sphericity (circularity) 18.13: An example with computational formulas 18.14: Another example: proactive interference 18.15: Key notions of the chapter 18.16: New notations 18.17: Key formulas of the chapter 18.18: Key questions of the chapter 19 ANOVA, Ttwo Factors Completely Repeated Measures: SxAxB 19.1: Introduction 19.2: Example: Plungin'! 19.3: Sum of Squares, Means squares and F ratios 19.4: Score model (Model I), SxAxB design: A and B fixed 19.5: Results of the experiment: Plungin' 19.6: Score Model (Model II): SxAxB design, A and B random 19.7: Score Model (Model III): SxAxB design, A fixed, B random 19.8: Quasi-F: F' 19.9: A cousin F'' 19.10: Validity assumptions, measures of intensity, key notions, etc 19.11: New notations 19.12: Key formulas of the chapter 20 ANOVA Two Factor Partially Repeated Measures: S(A)xB 20.1: Introduction 20.2: Example: Bat and Hat 20.3: Sums of Squares, Mean Squares, and F ratio 20.4: The comprehension formula routine 20.5: The 13 point computational routine 20.6: Score model (Model I), S(A)xB design: A and B fixed 20.7: Score model (Model II), S(A)xB design: A and B random 20.8: Score model (Model III), S(A)xB design: A fixed and B random 20.9: Coefficients of Intensity 20.10: Validity of S(A)xB designs 20.11: Prescription 20.12: New notations 20.13: Key formulas of the chapter 20.14: Key questions of the chapter 21 ANOVA, Nested Factorial Designs: SxA(B) 21.1: Introduction 21.2: Example: Faces in Space 21.3: How to analyze an SxA(B) design 21.4: Back to the example: Faces in Space 21.5: What to do with A fixed and B fixed 21.6: When A and B are random factors 21.7: When A is fixed and B is random 21.8: New notations 21.9: Key formulas of the chapter 21.10: Key questions of the chapter 22 How to derive expected values for any design 22.1: Introduction 22.2: Crossing and nesting refresher 22.3: Finding the sources of variation 22.4: Writing the score model 22.5: Degrees of freedom and sums of squares 22.6: Example 22.7: Expected values 22.8: Two additional exercises A Descriptive Statistics B The sum sign: E C Elementary Probability: A Refresher D Probability Distributions E The Binomial Test F Expected Values Statistical tables"

Reviews

`The structure of the book makes a lot of sense, and the chapters I have seen are well-written. ' David Lane, Rice University `Overall, I think the text has the potential to be an effective one...The writing style is excellent, and the amount and quality of in-text supporting material is excellent as well.' James Bovaird, University of Nebraska-Lincoln

The structure of the book makes a lot of sense, and the chapters I have seen are well-written. David Lane, Rice University Overall, I think the text has the potential to be an effective one...The writing style is excellent, and the amount and quality of in-text supporting material is excellent as well. James Bovaird, University of Nebraska-Lincoln

Overall, I think the text has the potential to be an effective one...The writing style is excellent, and the amount and quality of in-text supporting material is excellent as well. * James Bovaird, University of Nebraska-Lincoln * The structure of the book makes a lot of sense, and the chapters I have seen are well-written. * David Lane, Rice University *

Author Information

Herve Abdi He is currently a full professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas and an adjunct professor of radiology at the University of Texas Southwestern Medical Center at Dallas. His research interests include face processing and computational models of face processing, neural networks, computational and statistical models of cognitive processes (especially memory and learning), experimental design, and multivariate statistical analysis. He has published several books and papers in these domains. Betty Edelman teaches Statistics for Psychology and Research Design and Analysis as a senior lecturer at the University of Texas at Dallas. Her interests include modeling of cognitive processes using neural networks. She is a co-author of several research articles and a book about neural networks. Dominique Valentin is currently associate professor at the University of Bourgogne at Dijon, France. She has published a book and several papers dealing with neural networks and modeling. W. Jay Dowling is a professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas. His research interests have centred on the psychological reality and relevance to perception and memory of patterns of musical organization.

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