Privitera, G: Statistics for the Behavioral Sciences

relationship between multiple outcomes 5.5 Conditional probabilities and Bayes' Theorem 5.6 SPSS in Focus: Probability tables 5.7 Probability distributions 5.8 The mean of a probability distribution and expected value 5.9 Research in Focus: When are risks worth taking? 5.10 The variance and standard deviation of a probability distribution 5.11 Expected value and the binomial distribution 5.12 A final thought on the likelihood of random behavioral outcomesChapter 6- Probability and Normal Distributions 6.1 The normal distribution in behavioral science 6.2 Characteristics of the normal distribution 6.3 Research in Focus: The statistical norm 6.4 The standard normal distribution 6.5 The unit normal table: A brief introduction 6.6 Locating proportions 6.7 Locating scores 6.8 SPSS in Focus: Converting raw scores to standard z-scores 6.9 Going from binomial to normal 6.10 The normal approximation to the binomial distributionChapter 7- Probability and Sampling Distributions 7.1 Selecting samples from populations 7.2 Selecting a sample: Who's in and who's out? 7.3 Sampling distributions: The mean 7.4 Sampling distributions: The variance 7.5 The standard error of the mean 7.6 Factors that decrease standard error 7.7 SPSS in Focus: Estimating the standard error of the mean 7.8 APA in Focus: Reporting the standard error 7.9 Standard normal transformations with sampling distributionsChapter 8- Introduction to Hypothesis Testing 8.1 Inferential Statistics and hypothesis testing 8.2 Four steps to hypothesis testing 8.3 Hypothesis testing and sampling distributions 8.4 Making a decision: Types of error 8.5 Testing a research hypothesis: Examples using the z-test 8.6 Research in Focus: Directional versus non-directional tests 8.7 Measuring the size of an effect: Cohen's d 8.8 Effect size, power, and sample size 8.9 Additional factors that increase power 8.10 SPSS in Focus: A preview for Chapters 9 to 18 8.11 APA in Focus: Reporting the test statistic and effect sizeChapter 9-Testing Means: Independent Sample t-Tests 9.1 Going from z to t 9.2 The degrees of freedom 9.3 Reading the t-table 9.4 One-independent sample t-test 9.5 Effect size for the one-independent sample t-test 9.6 SPSS in Focus: One-independent sample t-test 9.7 Two-independent sample t-test 9.8 Effect size for the two-independent sample t-test 9.9 SPSS in Focus: Two-independent sample t-test 9.10 APA in Focus: Reporting the t-statistic and effect sizeChapter 10-Testing Means: Related Samples t-Test 10.1 Related and independent samples 10.2 Introduction to the related samples t-test 10.3 Related samples t-test: Repeated measures design 10.4 SPSS in Focus: The related samples t-test 10.5 Related samples t-test: Matched pairs design 10.6 Measuring effect size for the related samples t-test 10.7 Advantages for selecting related samples 10.8 APA in Focus: Reporting the t-statistic and effect size for related samplesChapter 11-Estimation and Confidence Intervals 11.1 Point estimation and interval estimation 11.2 The process of estimation 11.3 Estimation for the one-independent sample z-test 11.4 Estimation for the one-independent sample t-test 11.5 SPSS in Focus: Confidence intervals for the one-independent t-test 11.6 Estimation for the two-independent sample t-test 11.7 SPSS in Focus: Confidence intervals for the two-independent t-test 11.8 Estimation for the related samples t-test 11.9 SPSS in Focus: Confidence intervals for the related samples t-test 11.10 Characteristics of estimation: Precisions and certainty 11.11 APA in Focus: Reporting confidence intervalsChapter 12-Analysis of Variance: One-Way Between-Subjects Design 12.1 Increasing k: A shift to analyzing variance 12.2 An introduction to analysis of variance 12.3 Sources of variation and the test statistic 12.4 Degrees of freedom 12.5 The one-way between-subjects ANOVA 12.6 What is the next step? 12.7 Post hoc comparisons 12.8 SPSS in Focus: The one-way between-subjects ANOVA 12.9 Measuring effect size 12.10 APA in Focus: Reporting the F-statistic, significance, and effect sizeChapter 13-Analysis of Variance: One-Way Within-Subjects Design 13.1 Observing the same participants across treatments 13.2 Sources of variation and the test statistic 13.3 Degrees of freedom 13.4 The one-way within-subjects ANOVA 13.5 Post hoc comparison: Bonferroni procedure 13.6 SPSS in Focus: The one-way within-subjects ANOVA 13.7 Measuring effect size 13.8 The within-subjects design: Consistency and power 13.9 APA in Focus: Reporting the F-statistic, significance, and effect sizeChapter 14-Analysis of Variance: Two-Way Between-Subjects Factorial Design 14.1 Observing two factors at the same time 14.2 New terminology and notation 14.3 Designs for the two-way ANOVA 14.4 Describing variability: Main effects and interactions 14.5 The two-way between-subjects ANOVA 14.6 Analyzing main effects and interactions 14.7 Measuring effect size 14.8 SPSS in Focus: The two-way between-subjects ANOVA 14.9 APA in Focus: Reporting main effects, interactions, and effect sizeChapter 15-Correlation 15.1 Treating factors as dependent measures 15.2 Describing a correlation 15.3 Pearson correlation coefficient 15.4 SPSS in Focus: Pearson correlation coefficient 15.5 Assumptions of tests for linear correlations 15.6 Limitations in interpretation: Causality, outliers, and restriction of range 15.7 Alternative to Pearson r: Spearman correlation coefficient 15.8 SPSS in Focus: Spearman correlation coefficient 15.9 Alternative to Pearson r: Point-biserial correlation coefficient 15.10 SPSS in Focus: Point-biserial correlation coefficient 15.11 Alternative to Pearson r: Phi correlation coefficient 15.12 SPSS in Focus: Phi correlation coefficient 15.13 APA in Focus: Reporting correlationsChapter 16-Linear Regression 16.1 From relationships to predictions 16.2 Fundamentals of linear regression 16.3 What makes the regression line the best fitting line? 16.4 The slope and y-intercept of a straight line 16.5 Using the method of least squares to find the best fit 16.6 Using analysis of regression to measure significance 16.7 SPSS in Focus: Analysis of regression 16.8 Using the standard error of estimate to measure accuracy 16.9 Multiple regression 16.10 APA in Focus: Reporting regression analysisChapter 17-Nonparametric Tests: Chi-Square Tests 17.1 Tests for nominal data 17.2 The chi-square goodness-of-fit test 17.3 SPSS in Focus: The chi-square goodness-of-fit test 17.4 Interpreting the chi-square goodness-of-fit test 17.5 Independent observations and expected frequency size 17.6 The chi-square test for independence 17.7 The relationship between chi-square and the phi coefficient 17.8 Using the phi coefficient as a measure for effect size 17.9 SPSS in Focus: The chi-square test for independence 17.10 APA in Focus: Reporting the chi-square testChapter 18-Nonparametric Tests: Tests For Ordinal Data 18.1 Tests for ordinal data 18.2 The sign test 18.3 SPSS in Focus: The related samples sign test 18.4. The Wilcoxon signed-ranks T test 18.5 SPSS in Focus: The Wilcoxon signed-ranks T test 18.6 The Mann-Whitney U test 18.7 SPSS in Focus: The Mann-Whitney U test 18.8 The Kruskal-Wallis H test 18.9 SPSS in Focus: The Kruskal-Wallis H test 18.10 The Friedman test 18.11 SPSS in Focus: The Friedman test 18.12 APA in Focus: Reporting nonparametric testsAppendix A-Mathematics in Statistics A.1 Positive and negative numbers A.2 Addition A.3 Subtraction A.4 Multiplication A.5 Division A.6 Fractions A.7 Decimals and percents A.8 Exponents and roots A.9 Order of computation A.10 Equations: Solving for x A.11 Summation notationAppendix B-Statistical Tables Table B.1 Unit Normal Table Table B.2 The t Table Table B.3 The F Table Table B.4 Studentized Range Statistic Table Table B.5 The Pearson Correlation Table Table B.6 The Spearman Correlation Table Table B.7 The Chi-square Table Table B.8 Binomial Probability Distribution Table Table B.9 The Wilcoxon T Table Table B.10 The Mann-Whitney U TableAppendix C-Chapter Solutions For Even Numbered Problems