| Description |
1 online resource (xx, 538 pages) : illustrations |
| Series |
Psychological experiments online |
| 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 |
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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 |
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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 |
| Summary |
"Careful data collection and analysis lies at the heart of good research, through which our understanding of psychology is enhanced. Yet the students who will become the next generation of researchers need more exposure to statistics and experimental design than a typical introductory course presents." "Experimental Design and Analysis for Psychology provides a complete course in data collection and analysis for students who need to go beyond the basics." "Acting as a true course companion, the text's engaging writing style leads readers through a range of often challenging topics, blending examples and exercises with careful explanations and custom-drawn figures to ensure even the most daunting concepts can be fully understood." "Opening with a review of key concepts, including probability, correlation, and regression, the book goes on to explore the analysis of variance and factorial designs, before moving on to consider a range of more specialised, but yet powerful, statistical tools, including the General Linear Model, and the concept of unbalanced designs."--Jacket |
| Analysis |
Psychology, Experimental Research Methodology |
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Psychometrics |
| Bibliography |
Includes bibliographical references (pages 518-530) and index |
| Notes |
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL |
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In English |
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digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL |
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Print version record |
| Subject |
Psychology, Experimental -- Research -- Methodology
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Psychometrics.
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Psychology, Experimental.
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Analysis of variance.
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Psychology, Experimental
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Analysis of Variance
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Psychometrics
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Research Design
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Psychology, Experimental
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Analysis of variance
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Psychology, Experimental -- Research -- Methodology
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Psychometrics
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Experimentelle Versuchsforschung
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Datenanalyse
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Psychologie
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Experimentell psykologi -- forskning -- metodik.
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| Form |
Electronic book
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| Author |
Abdi, Hervé.
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