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