Straightforward Statistics

Preface1. 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 class2. 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 deviation3. 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- Summary4. 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- Summary5. 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- Summary6. The Basic Elements of Hypothesis Testing- Probability- The normal distribution- Estimating likelihood of outcomes- A real research example- Summary7. 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- Summary8. 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- Summary9. 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- Summary10. 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- Summary11. 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- Summary12. 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- Summary13. 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- SummaryAppendix A: Normal Curve (Z) TableAppendix B: t TableAppendix C: F TableAppendix D: Chi Square TableAppendix E: Advanced Statistics You May Run Into- Factor Analysis- Multiple regression- Structural Equation Modeling- Repeated-Measures ANOVA- Mixed-Design ANOVA - MANOVAAppendix 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 ANOVAGlossaryAnswers to Set B Homework ProblemsReferences