Experience the next generation
Join the IBM SPSS Statistics early access program to help shape a reimagined SPSS Statistics, featuring the most popular capabilities for beginner and intermediate users.
Overview
IBM® SPSS® Statistics is a powerful statistical software platform. It offers a user-friendly interface and a robust set of features that lets your organization quickly extract actionable insights from your data. Advanced statistical procedures help ensure high accuracy and quality decision making. All facets of the analytics lifecycle are included, from data preparation and management to analysis and reporting.
SPSS Statistics academic editions
For institutions & administrators
For students and faculty
Explore what's new with SPSS Statistics 28.0.1
Benefits
Easy to use
Integrated with open source
Comprehensive
Flexible
Purchase options
Features
Intuitive user interface
Perform powerful analyses without coding experience using a drag-and-drop interface.
Advanced data visualizations
Build visualizations and easily export to include in multiple file formats to communicate results effectively.
Automated data preparation
Help ensure data is clear, properly organized and ready for analysis.
Efficient data conditioning
Identify invalid values, view patterns of missing data and summarize variable distributions.
Local data storage
Increase data security by storing files and data on your computer rather than in the cloud.
What’s new
SPSS Statistics 28: Latest release
New statistical algorithms, procedural enhancements and usability improvement to boost data analysis
Tech Talk series
Tips for SPSS Statistics 28 to help both statistics novices and experts unlock richer insights from data
Learning guide
Videos, product tours, tutorials and more to help you accelerate data analysis with SPSS Statistics
Product images
Bayesian procedures
Bayesian procedures
Estimate Bayes factors and posterior distributions for parameters.
Multilayer perceptron (MLP) network
Multilayer perceptron (MLP) network
Predict or classify outcomes using neural network models.
Estimated marginal means
Estimated marginal means
Compare group means using a general linear model approach.