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