Course Outline
1. Introduction to statistical analysis
- Identify the steps in the research process
- Principles of statistical analysis
2. Examine individual variables
- Identify measurement levels
- Chart individual variables
- Summarize individual variables
- Examine the normal distribution
- Examine standardized scores
3. Test hypotheses about individual variables
- Identify population parameters and sample statistics
- Examine the distribution of the sample mean
- Determine the sample size
- Test a hypothesis on the population mean
- Construct a confidence interval for the population mean
- Tests on a single variable: One-Sample T Test, Paired-Samples T Test, and Binomial Test
4. Test the relationship between categorical variables
- Chart the relationship between two categorical variables
- Describe the relationship: Compare percentages in Crosstabs
- Test the relationship: The Chi-Square test in Crosstabs
- Assumptions of the Chi-Square test
- Pairwise compare column proportions
- Measure the strength of the association
5. Test on the difference between two group means
- Compare the Independent-Samples T Test to the Paired-Samples T Test
- Chart the relationship between the group variable and scale variable
- Describe the relationship: Compare group means
- Test on the difference between two group means: Independent-Samples T Test
- Assumptions of the Independent-Samples T Test
6. Test on differences between more than two group means
- Describe the relationship: Compare group means
- Test the hypothesis of equal group means: One-Way ANOVA
- Assumptions of One-Way ANOVA
- Identify differences between group means: Post-hoc tests
7. Test the relationship between scale variables
- Chart the relationship between two scale variables
- Describe the relationship: Correlation
- Test on the correlation
- Assumptions for testing on the correlation
- Treatment of missing values
8. Predict a scale variable: Regression
- What is linear regression?
- Explain unstandardized and standardized coefficients
- Assess the fit of the model: R Square
- Examine residuals
- Include 0-1 independent variables
- Include categorical independent variables
9. Introduction to Bayesian statistics
- Bayesian statistics versus classical test theory
- Explain the Bayesian approach
- Evaluate a null hypothesis: Bayes Factor
- Bayesian procedures in IBM SPSS Statistics
10. Overview of multivariate procedures
- Overview of supervised models
- Overview of models to create natural grouping