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Preface; 1: Why Do I Need to Learn Statistics?; - Examples of statistics in the real world; - The nature of findings and facts in the behavioral sciences; - Descriptive and Inferential Statistics; - A conceptual approach to teaching and learning statistics; - What you should get out of this class; 2. Describing a Single Variable; - The nature of variables: continuous vs. categorical; - Frequency distributions as descriptions of single variables; - Creating frequency distributions; - Representing frequency distributions graphically; - Interpreting frequency distributions; - Mean, median, and mode; - Why is the mean the most-utilized index of central tendency?; - The conceptual elements of standard deviation; - Computing standard deviation; 3. Standardized Scores; - Why are standardized scores needed in the real world?; - Why are standardized scores needed in statistics?; - Computing Z scores; - Interpreting Z scores; - A real research example; - Summary; 4. Correlation; - Real-world examples of correlations; - Representing correlations graphically (the scatterplot); - Representing correlations quantitatively (Pearson’s r: an index of correlation strength and direction); - Computing r using Z-scores; - Interpreting r (what you can and cannot conclude knowing that a correlation between two variables exists); - A real research example; - Summary; 5. Statistical Prediction and Regression; - The basic rationale underlying regression; - Standardized model of bivariate regression; - Raw-score model of bivariate regression; - The regression line; - Estimating error of prediction; - Basic rationale underlying multiple regression; - A real research example; - Summary; 6. The Basic Elements of Hypothesis Testing; - Probability; - The normal distribution; - Estimating likelihood of outcomes; - A real research example; - Summary; 7. Introduction to Hypothesis Testing; - Basic rationale underlying hypothesis testing; - What is meant by statistical significance?; - The five steps of hypothesis testing:; - Stating the null and research hypotheses; - Delineating the nature of the comparison distribution; - Determining alpha (by defining a part of the comparison distribution is highly unlikely); - Comparing a sample from the special population with the comparison distribution; - Commenting on the null hypothesis; - A real research example; - Summary; 8. Hypothesis Testing if N > 1; - The basic steps of hypothesis testing always remain the same; - The comparison distribution needed for comparing a sample mean: The distribution of means; - Hypothesis testing using the distribution of means; - Confidence intervals; - A real research example; - Summary; 9. Statistical Power; - Defining Power (p(rejecting the null hypothesis when the research hypothesis is true) and Beta (p(Type-II error)); - How N, population-level standard deviation, and effect size affect power; - Computing power; - How power affects real research; - A real research example; - Summary; 10. t-tests (One-Sample and Within-Groups); - How a t-test differs from a Z-test; - The nature of the t-distribution (and why it varies as it does); - Computing a one-sample t-test; - Computing a repeated-measures t-test; - A real research example; - Summary; 11. t-tests: Between-Groups; - The basic rationale of the between-groups t-test; - Computing the between-groups t-test; - Interpreting results; - A real research example; - Summary; 12. Analysis of Variance; - Basic reasoning of F as a ratio between effect and error variance; - Concepts underlying a one-way ANOVA; - Computing a one-way ANOVA; - Factorial ANOVA; - What results from an ANOVA can and cannot tell you; - Post-hoc tests; - A real research example; - Summary; 13. Chi-Square; - What happens when all our variables are categorical?; - Basic rationale underlying goodness of fit test; - Computing the chi-square goodness of fit; - Computing the chi-square test of independence; - Interpreting chi-square results; - A real research example; - Summary; Appendix A: Normal Curve (Z) Table; Appendix B: t Table; Appendix C: F Table; Appendix D: Chi Square Table; Appendix E: Advanced Statistics You May Run Into; - Factor Analysis; - Multiple regression; - Structural Equation Modeling; - Repeated-Measures ANOVA; - Mixed-Design ANOVA; - MANOVA; Appendix F: Using SPSS to Compute Basic Statistics; - Benefits of SPSS; - Different kinds of SPSS files; - Entering data with SPSS; - Computing frequency distributions with SPSS; - Describing variables with SPSS; - Using SPSS to examine correlations; - Using SPSS for a repeated-measures test; - Using SPSS for a between-groups test; - Using SPSS for a one-way ANOVA; Glossary; Answers to Set B Homework Problems; References; Index

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