TY  - BOOK
AU  - Abdi,Hervé
TI  - Experimental design and analysis for psychology
SN  - 0199299889
AV  - BF181 .E85 2009
U1  - 150.724 22
PY  - 2009///
CY  - New York
PB  - Oxford University Press
KW  - Psychology, Experimental
KW  - Research
KW  - Methodology
KW  - Psychometrics
KW  - Analysis of variance
KW  - Research Design
N1  - 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
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