1 Introduction to Experimental Design1.1: Introduction1.2: Independent and dependent variables1.3: Independent variables1.4: Dependent variables1.5: Choice of subjects and representative design of experiments1.7: Key notions of the chapter2 Correlation2.1: Introduction2.
2: Correlation: Overview and Example2.3: Rationale and computation of the coefficient of correlation2.4: Interpreting correlation and scatterplots2.5: The importance of scatterplots2.6: Correlation and similarity of distributions2.7: Correlation and Z-scores2.8: Correlation and causality2.9: Squared correlation as common variance2.
10: Key notions of the chapter2.11: Key formulas of the chapter2.12: Key questions of the chapter3 Statistical Test: The F test3.1: Introduction3.2: Statistical Test3.3: Not zero is not enough!3.4: Key notions of the chapter3.5: New notations3.
6: Key formulas of the chapter3.7: Key questions of the chapter4 Simple Linear Regression4.1: Introduction4.2: Generalities4.3: The regression line is the "best-fit" line4.4: Example: Reaction Time and Memory Set4.5: How to evaluate the quality of prediction4.6: Partitioning the total sum of squares4.
7: Mathematical Digressions4.8: Key notions of the chapter4.9: New notations4.10: Key formulas of the chapter4.11: Key questions of the chapter5 Orthogonal Multiple Regression5.1: Introduction5.2: Generalities5.3: The regression plane is the "best-fit" plane5.
4: Back to the example: Retroactive interference5.5: How to evaluate the quality of the prediction5.6: F tests for the simple coefficients of correlation5.7: Partitioning the sums of squares5.8: Mathematical Digressions5.9: Key notions of the chapter5.10: New notations5.11: Key formulas of the chapter5.
12: Key questions of the chapter6 Non-Orthogonal Multiple Regression6.1: Introduction6.2: Example: Age, speech rate and memory span6.3: Computation of the regression plane6.4: How to evaluate the quality of the prediction6.5: Semi-partial correlation as increment in explanation6.5: F tests for the semi-partial correlation coefficients6.6: What to do with more than two independent variables6.
7: Bonus: Partial correlation6.8: Key notions of the chapter6.9: New notations6.10: Key formulas of the chapter6.11: Key questions of the chapter7 ANOVA One Factor: Intuitive Approach and Computation of F7.1: Introduction7.2: Intuitive approach7.3: Computation of the F ratio7.
4: A bit of computation: Mental Imagery7.5: Key notions of the chapter7.6: New notations7.7: Key formulas of the chapter7.8: Key questions of the chapter8 ANOVA, One Factor: Test, Computation, and Effect Size8.1: Introduction8.2: Statistical test: A refresher8.3: Example: back to mental imagery8.
4: Another more general notation: A and S(A)8.5: Presentation of the ANOVA results8.6: ANOVA with two groups: F and t8.7: Another example: Romeo and Juliet8.8: How to estimate the effect size8.9: Computational formulas8.10: Key notions of the chapter8.11: New notations8.
12: Key formulas of the chapter8.13: Key questions of the chapter9 ANOVA, one factor: Regression Point of View9.1: Introduction9.2: Example 1: Memory and Imagery9.3: Analysis of variance for Example 19.4: Regression approach for Example 1: Mental Imagery9.5: Equivalence between regression and analysis of variance9.6: Example 2: Romeo and Juliet9.
7: If regression and analysis of variance are one thing, why keep two different techniques?9.8: Digression: when predicting Y from Ma., b=19.9: Multiple regression and analysis of variance9.10: Key notions of the chapter9.11: Key formulas of the chapter9.12: Key questions of the chapter10 ANOVE, one factor: Score Model10.1: Introduction10.
2: ANOVA with one random factor (Model II)10.3: The Score Model: Model II10.4: F < 1 or The Strawberry Basket10.5: Size effect coefficients derived from the score model: w2 and p210.6: Three exercises10.7: Key notions of the chapter10.8: New notations10.9: Key formulas of the chapter10.
10: Key questions of the chapter11 Assumptions of Analysis of Variance11.1: Introduction11.2: Validity assumptions11.3: Testing the Homogeneity of variance assumption11.4: Example11.5: Testing Normality: Lilliefors11.6: Notation11.7: Numerical example11.
8: Numerical approximation11.9: Transforming scores11.10: Key notions of the chapter11.11: New notations11.12: Key formulas of the chapter11.13: Key questions of the chapter12 Analysis of Variance, one factor: Planned Orthogonal Comparisons12.1: Introduction12.2: What is a contrast?12.
3: The different meanings of alpha12.4: An example: Context and Memory12.5: Checking the independence of two contrasts12.6: Computing the sum of squares for a contrast12.7: Another view: Contrast analysis as regression12.8: Critical values for the statistical index12.9: Back to the Context12.10: Significance of the omnibus F vs.
significance of specific contrasts12.11: How to present the results of orthogonal comparisons12.12: The omnibus F is a mean12.13: Sum of orthogonal contrasts: Subdesign analysis12.14: Key notions of the chapter12.15: New notations12.16: Key formulas of the chapter12.17: Key questions of the chapter13 ANOVA, one factor: Planned Non-orthogonal Comparisons13.
1: Introduction13.2: The classical approach13.3: Multiple regression: The return!13.4: Key notions of the chapter13.5: New notations13.6: Key formulas of the chapter13.7: Key questions of the chapter14 ANOVA, one factor: Post hoc or a posteriori analyses14.1: Introduction14.
2: Scheffe''s test: All possible contrasts14.3: Pairwise comparisons14.4: Key notions of the chapter14.5: New notations14.6: Key questions of the chapter15 More on Experimental Design: Multi-Factorial Designs15.1: Introduction15.2: Notation of experimental designs15.3: Writing down experimental designs15.
4: Basic experimental designs15.5: Control factors and factors of interest15.6: Key notions of the chapter15.7: Key questions of the chapter16 ANOVA, two factors: AxB or S(AxB)16.1: Introduction16.2: Organization of a two-factor design: AxB16.3: Main effects and interaction16.4: Partitioning the experimental sum of squares16.
5: Degrees of freedom and mean squares16.6: The Score Model (Model I) and the sums of squares16.7: An example: Cute Cued Recall16.8: Score Model II: A and B random factors16.9: ANOVA AxB (Model III): one factor fixed, one factor random16.10: Index of effect size16.11: Statistical assumptions and conditions of validity16.12: Computational formulas16.
13: Relationship between the names of the sources of variability, df and SS16.14: Key notions of the chapter16.15: New notations16.16: Key formulas of the chapter16.17: Key questions of the chapter17 Factorial designs and contrasts17.1: Introduction17.2: Vocabulary17.3: Fine grained partition of the standard decomposition17.
4: Contrast analysis in lieu of the standard decomposition17.5: What error term should be used?17.6: Example: partitioning the standard decomposition17.7: Example: a contrtast non-orthogonal to the canonical decomposition17.8: A posteriori Comparisons17.9: Key notions of the chapter17.10: Key questions of the chapter18 ANOVA, one factor Repeated Measures design: SxA18.1: Introduction18.
2: Advantages of repeated measurement designs18.3: Examination of the F Ratio18.4: Partitioning the within-group variability: S(A) = S + SA18.5: Computing F in an SxA design18.6: Numerical example: SxA design18.7: Score Model: Models I and II for repeated measures designs18.8: Effect size: R, R and R18.9: Problems with repeated measures18.
10: Score model (Model I) SxA design: A fixed18.11: Score model (Model II) SxA design: A random18.12: A new assumption: sphericity (circularity)18.13: An example with computational formulas18.14: Another example: proactive interference18.15: Key notions of the chapter18.16: New notations18.17: Key formulas of the chapter18.
18: Key questions of the chapter19 ANOVA, Ttwo Factors Completely Repeated Measures: SxAxB19.1: Introduction19.2: Example: Plungin''!19.3: Sum of Squares, Means squares and F ratios19.4: Score model (Model I), SxAxB design: A and B fixed19.5: Results of the experiment: Plungin''19.6: Score Model (Model II): SxAxB design, A and B random19.7: Score Model (Model III): SxAxB design, A fixed, B random19.
8: Quasi-F: F''19.9: A cousin F''''19.10: Validity assumptions, measures of intensity, key notions, etc19.11: New notations19.12: Key formulas of the chapter20 ANOVA Two Factor Partially Repeated Measures: S(A)xB20.1: Introduction20.2: Example: Bat and Hat20.3: Sums of Squares, Mean Squares, and F ratio20.
4: The comprehension formula routine20.5: The 13 point computational routine20.6: Score model (Model I), S(A)xB design: A and B fixed20.7: Score model (Model II), S(A)xB design: A and B random20.8: Score model (Model III), S(A)xB design: A fixed and B random20.9: Coefficients of Intensity20.10: Validity of S(A)xB designs20.11: Prescription20.
12: New notations20.13: Key formulas of the chapter20.14: Key questions of the chapter21 ANOVA, Nested Factorial Designs: SxA(B)21.1: Introduction21.2: Example: Faces in Space21.3: How to analyze an SxA(B) design21.4: Back to the example: Faces in Space21.5: What to do with A fixed and B fixed21.
6: When A and B are random factors21.7: When A is fixed and B is random21.8: New notations21.9: Key formulas of the chapter21.10: Key questions of the chapter22 How to derive expected values for any design22.1: Introduction22.2: Crossing and nesting refresher22.3: Finding the sources of variation22.
4: Writing the score model22.5: Degrees of freedom and sums of squares22.6: Example22.7: Expected values22.8: Two additional exercisesA Descriptive StatisticsB The sum sign: EC Elementary Probability: A RefresherD Probability DistributionsE The Binomial TestF Expected ValuesStatistical tables.