Detailed Course Outline
1. Introduction to statistical analysis • Explain the difference between a sample and a population • Explain the difference between an experimental research design and a non-experimental research design • Explain the difference between independent and dependent variables 2. Examine individual variables • Describe the levels of measurement used in IBM SPSS Statistics • Use graphs to examine variables • Use summary measures to examine variables • Explain normal distributions • Explain standardized scores and their use 3. Test hypotheses-theory • Explain the difference between a sample and a population • Design a test of a hypothesis • Explain the alpha level • Explain the difference between statistical and practical significance • Describe the two types of errors in testing a hypothesis 4. Test hypotheses about individual variables • Explain the sampling distribution of a statistic • Explain the difference between the standard deviation and the standard error • Use the One-Sample T Test to test a hypothesis about a population mean • Use the Paired-Samples T Test to test on a "before-after treatment" effect • Use the Binomial Test to test a hypothesis about a population proportion 5. Test the relationship between categorical variables • Use the Chart Builder to graphically illustrate the relationship between two categorical variables • Use percentages in Crosstabs to describe the relationship between two categorical variables • Use the Chi-Square test in Crosstabs to test the relationship between two categorical variables 6. Test the difference between two group means • Use the Chart Builder to graphically illustrate the difference between two group means • Use Explore and Means to describe the differences between groups • Use the Independent-Samples T Test to test whether the difference between two group means is statistically significant 7. Test the differences between more than two group means • Use One-Way ANOVA to determine whether there are statistically significant differences between means of three or more groups • Use post hoc tests to detect differences between group means 8. Test the relationship between scale variables • Chart the relationship between two scale variables • Use Pearsons correlation coefficient to examine the relationship between two scale variables • Test hypotheses on Pearsons correlation coefficient • Assumptions for using Pearsons correlation coefficient 9. Predict a scale variable • Use Linear Regression to predict a scale variable with one or more scale variables • Use Automatic Linear Modeling to predict a scale variable with categorical and scale variables 10. Explore nonparametric tests • Describe when Nonparametric Tests should and can be used • Use Nonparametric Tests for two or more independent samples • Use Nonparametric Tests for two dependent samples