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Part I: Statistical Ideas Chapter 1: Getting started with SPSS and using the computer for experimental details 1.1 Getting started with SPSS 1.2 Manipulating variables 1.3 Random Number Generation 1.4 Summary Chapter 2: Some Preliminaries to Understanding Statistics 2.1 Variables 2.2 Understanding hidden assumptions about how statistical testing works 2.3 Parametric and nonparametric statistics Chapter 3: Describing data numerically and graphically & assessing assumptions for parametric tests 3.1 Numerical Summaries of Data 3.2 Using SPSS to get numerical summaries 3.3 Satisfying assumptions for parametric tests 3.4 Graphic summaries of data: examining the shape of distributions for normality 3.5 Examining the shape of distributions: The assumption of homogeneity 3.6 Dealing with departures from expectations 3.7 Summary Chapter 4: Changing the way we do statistics: Hypothesis testing, power, effect size, and other misunderstood issues 4.1 Null hypothesis significance tests 4.2 Power Analysis 4.3 Effect Size 4.4 Confidence Intervals 4.5 Summary   Part II: Statistical Tests Chapter 5: Choosing a statistical test 5.1 Statistical tests that are covered in this book 5.2 A brief overview of correlation 5.3 A brief overview of partial correlation 5.4 A brief overview of multiple regression 5.5 A brief overview of the chi-square test of independence 5.6 A brief overview of t-tests 5.7 A brief overview of one-way ANOVA 5.8 A brief overview of factorial ANOVA 5.9 A brief overview of ANCOVA 5.10 A brief overview of repeated measures ANOVA 5.11 Summary 5.12 Application activity for choosing a statistical test Chapter 6: Finding relationships using correlation: Age of learning 6.1 Visual Inspection: Scatterplots 6.2 Creating Scatterplots 6.3 Assumptions of parametric statistics for correlation 6.4 Calculating correlation coefficients 6.5 Other types of correlations (Advanced topic) 6.6 Summary Chapter 7: Looking for groups of explanatory variables through multiple regression: Predicting important factors in first-grade reading 7.1 Understanding regression design 7.2 Visualizing multiple relationships 7.3 Assumptions of Multiple Regression 7.4 Performing a multiple regression 7.5 Taking regression further: Finding the best fit (Advanced topics) 7.6 Summary Chapter 8: Finding group differences with chi-square when all your variables are categorical: The effects of interaction feedback on question formation and the choice of copular verb in Spanish 8.1 Two types of chi-square tests 8.2 Data Inspection: Tables and Crosstabs 8.3 Visualizing categorical data 8.4 Assumptions of chi-square 8.5 Chi-square statistical test 8.6 Summary of chi-square Chapter 9: Looking for differences between two means with t-tests: Think-Aloud Methodology and Phonological Memory 9.1 Types of t-tests 9.2 Data summaries and numerical inspection 9.3 Assumptions of t-tests 9.4 The independent samples t-test 9.5 The paired-samples t-test 9.6 Reporting t-test results 9.7 Performing a one-sample t-test (Advanced topics) 9.8 Summary of t-tests Chapter 10: Looking for group differences with a one-wayAnalysis of Variance (ANOVA): Effects of planning time 10.1 Understanding ANOVA design 10.2 The topic of Chapter 10 10.3 Assumptions of ANOVA 10.4 One-way ANOVA 10.5 Performing omnibus one-way ANOVA tests in SPSS 10.6 Summary of one-way ANOVAs Chapter 11: Looking for group differences with factorial Analysis of Variance (ANOVA) when there is more than one independent variable: Learning with music 11.1 ANOVA design 11.2 Numerical and Visual Inspection 11.3 Assumptions of factorial ANOVA 11.4 Factorial ANOVAs: extending analyses to more than one independent variable 11.5 Summary Chapter 12: Looking for group differences when the same people are tested more than once using Repeated Measures ANOVA: Wug tests and instruction on French gender 12.1 Understanding repeated measures ANOVA designs variables to decide between RM ANOVA and Factorial ANOVA designs 12.2 Visualizing repeated measures 12.3 Repeated-measures ANOVA assumptions 12.4 Performing an RM ANOVA using SPSS 12.5 Summary Chapter 13: Factoring out differences with analysis of covariance: The effect of feedback on French gender 13.1 Visually and numerically examining the data 13.2 ANCOVA design 13.3 Assumptions of ANCOVA 13.4 Performing an ANCOVA 13.5 Summary Chapter 14: Statistics when your data do not satisfy parametric assumptions: Non-parametric statistics 14.1 Why use non-parametric statistics? 14.2 Non-parametric statistics in SPSS 14.3 Summary