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

SPSS Statistics academic editions

For institutions & administrators

For students and faculty

Explore what's new with SPSS Statistics 28.0.1

Benefits

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

Discriminant scores scatter

Screen shot showing a discriminant scores scatter in SPSS Statistics

Discriminant scores scatter

Classify groups based on measured characteristics.

Bayesian procedures

Screen shot showing Bayesian procedures in SPSS Statistics

Bayesian procedures

Estimate Bayes factors and posterior distributions for parameters.

Multilayer perceptron (MLP) network

Screen shot showing a multilayer perceptron network in SPSS Statistics

Multilayer perceptron (MLP) network

Predict or classify outcomes using neural network models.

Estimated marginal means

Screen shot showing an estimated marginal means bar chart in SPSS Statistics

Estimated marginal means

Compare group means using a general linear model approach.