SPSS: Wrangling, Visualizing, and Modeling Data
6h 3mIntermediate2025-06-26
Authors
Barton Poulson
Professor, Designer, Data Analytics Expert
Course details
SPSS is a common menu-driven statistical application used in the social sciences, education, and business. In this course, data analytics expert Barton Poulson shows you how to analyze data in ways that are difficult or impossible in spreadsheets, without requiring the full learning curve of a programming language like Python or R. Learn how to navigate SPSS, use sample datasets, set options and preferences, and more. Go over data visualization and data wrangling. Learn about recoding data and explore it using computing frequencies, computing descriptives, exploratory data analysis, computing correlations, and computing contingency tables. Go over ways to analyze data, including comparing proportions, comparing multiple means, computing a linear regression, and much more. Plus, learn how to share your work by exporting charts and tables.
Learning objectives
Explain how to import data into SPSS from various file formats, including CSV and Excel.
Describe the process of transforming data in SPSS by specifying variable types, measurement levels, and roles.
Create and customize visualizations such as bar charts, box plots, and scatter plots to explore data patterns and relationships.
Conduct exploratory data analysis (EDA) using the Frequencies, Descriptives, and Explore commands to summarize and understand data distributions.
Perform correlation analysis to evaluate the relationships between variables, including Pearson, Spearman, and Kendall’s tau correlations.
Generate cross-tabulations and chi-squared tests to examine associations between categorical variables.
Conduct inferential tests such as one-sample proportion tests, one-sample t-tests, paired t-tests, and independent samples t-tests to compare means and proportions.
Apply one-way and two-way ANOVA to examine differences in means across multiple groups and analyze interaction effects between factors.
Develop and interpret linear regression models to predict outcomes based on multiple predictor variables.
Export SPSS output to various formats, including Word, PDF, and HTML, to effectively share analysis results.
Learning objectives
Explain how to import data into SPSS from various file formats, including CSV and Excel.
Describe the process of transforming data in SPSS by specifying variable types, measurement levels, and roles.
Create and customize visualizations such as bar charts, box plots, and scatter plots to explore data patterns and relationships.
Conduct exploratory data analysis (EDA) using the Frequencies, Descriptives, and Explore commands to summarize and understand data distributions.
Perform correlation analysis to evaluate the relationships between variables, including Pearson, Spearman, and Kendall’s tau correlations.
Generate cross-tabulations and chi-squared tests to examine associations between categorical variables.
Conduct inferential tests such as one-sample proportion tests, one-sample t-tests, paired t-tests, and independent samples t-tests to compare means and proportions.
Apply one-way and two-way ANOVA to examine differences in means across multiple groups and analyze interaction effects between factors.
Develop and interpret linear regression models to predict outcomes based on multiple predictor variables.
Export SPSS output to various formats, including Word, PDF, and HTML, to effectively share analysis results.
Skills covered
SPSS StatisticsSPSSIBMData VisualizationData EngineeringData AnalysisData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
0. Introduction
- 01 - Welcome
- 02 - Using the exercise files
1. What is SPSS
- 03 - SPSS in context
- 04 - Versions, releases, licenses, and interfaces
2. Getting Started
- 05 - Navigating SPSS
- 06 - Sample datasets
- 07 - Data types, measures, and roles
- 08 - Options and preferences
- 09 - Extending SPSS
- 10 - Saving and running syntax files
- 11 - The SPSS workbook interface
3. Data Visualization
- 12 - Visualizing data with Chart Builder
- 13 - Modifying Chart Builder visualizations
- 14 - Visualizing data with Graphboard templates
- 15 - Modifying Graphboard visualizations
- 16 - Using legacy dialogs - Boxplots for multiple variables
- 17 - Creating regression variable plots
- 18 - Comparing subgroups
4. Data Wrangling
- 19 - Importing data
- 20 - Variable labels
- 21 - Value labels
- 22 - Splitting files
- 23 - Selecting cases and subgroups
5. Recoding Data
- 24 - Recoding variables
- 25 - Reversing values with syntax
- 26 - Recoding by ranking cases
- 27 - Creating dummy variables
- 28 - Recoding with visual binning
- 29 - Recoding with optimal binning
- 30 - Preparing data for modeling
- 31 - Computing scores
6. Exploring Data
- 32 - Computing frequencies
- 33 - Computing descriptives
- 34 - Exploratory data analysis
- 35 - Computing correlations
- 36 - Computing contingency tables
- 37 - Factor analysis and principal component analysis
- 38 - Reliability analysis
7. Clustering and Classification
- 39 - Hierarchical clustering
- 40 - k-means clustering
- 41 - k-nearest neighbors classification
- 42 - Decision tree classification in SPSS
- 43 - Neural networks in SPSS - Multilayer perceptron classification
- 44 - Neural networks in SPSS - Radial basis function classification
8. Analyzing Data
- 45 - Comparing proportions
- 46 - Comparing one mean to a population - One-sample t test
- 47 - Comparing paired means - Paired-samples t test
- 48 - Comparing two means - Independent-samples t test
- 49 - Comparing multiple means - One-way ANOVA
- 50 - Comparing means with two categorical variables - ANOVA
9. Building Predictive Models
- 51 - Computing a linear regression
- 52 - Variable selection
- 53 - Logistic regression
- 54 - Automatic linear modeling
10. Sharing Your Work
- 55 - Exporting charts and tables
Conclusion
- 56 - Next steps