SPSS Statistics Essential Training

Get up and running with SPSS Statistics. Learn how to work with the program to make data visualizations, calculate descriptive statistics, and more.

Introduction

• Welcome
• Using the exercise files

1. What Is SPSS?

• SPSS in context
• Versions, releases, licenses, and interfaces

2. Getting Started

• Navigating SPSS
• Sample datasets
• Data types, measures, and roles
• Options and preferences
• Extending SPSS
• Saving and running syntax files

3. Data Visualization

• Visualizing data with Chart Builder
• Modifying Chart Builder visualizations
• Visualizing data with Graphboard templates
• Modifying Graphboard visualizations
• Using legacy dialogs: Boxplots for multiple variables
• Creating regression variable plots
• Comparing subgroups

4. Data Wrangling

• Importing data
• Variable labels
• Value labels
• Splitting files
• Selecting cases and subgroups

5. Recoding Data

• Recoding variables
• Reversing values with syntax
• Recoding by ranking cases
• Creating dummy variables
• Recoding with Visual Binning
• Recoding with Optimal Binning
• Preparing data for modeling
• Computing scores

6. Exploring Data

• Computing frequencies
• Computing descriptives
• Exploratory data analysis
• Computing correlations
• Computing contingency tables
• Factor analysis and principal component analysis
• Reliability analysis

7. Clustering and Classification

• Hierarchical clustering
• k-means clustering
• k-nearest neighbors classification
• Decision tree classification in SPSS
• Neural networks in SPSS: Multilayer perceptron classification
• Neural networks in SPSS: Radial basis function classification

8. Analyzing Data

• Comparing proportions
• Comparing one mean to a population: One-sample t-test
• Comparing paired means: Paired-samples t-test
• Comparing two means: Independent-samples t-test
• Comparing multiple means: One-way ANOVA
• Comparing means with two categorical variables: ANOVA

9. Building Predictive Models

• Computing a linear regression
• Variable selection
• Logistic regression
• Automatic linear modeling

10. Sharing Your Work

• Exporting charts and tables
• Web reports

Conclusion

• Next steps