Experimental design and analysis for psychology / Hervé Abdi [and others].

Includes bibliographical references (pages 518-530) and index.

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.

Machine converted from AACR2 source record.