IBM SPSS Statistics Premium Campus Edition 1-Year Subscription for Mac/Windows (Download)

Empower your academic institution with just one license - simple and hassle-free access for every user.

IBM SPSS Statistics for academic institutions offers an all-in-one solution to enhance research, teaching, and learning across the board. Tailored to address the unique challenges of academic institutions, it offers a scalable solution that provides affordable access and meets the diverse needs of every user.

Reimagine what your institution can achieve:

• University-wide research - Enable collaborative, impactful research with shared analytics for students and faculty.
• Data-driven planning - Inform strategic decisions with insights from enrollment, performance, and retention data.
• Enhanced curriculum - Integrate data analysis into courses, building practical, career-ready skills.
• Insightful feedback analysis - Easily analyze campus surveys to improve student and faculty experiences.

Included in the Premium Edition are:

• Statistics Base
• Data Preparation: IBM® SPSS® Data Preparation performs advanced techniques to streamline the data preparation stage, delivering faster, more accurate data analysis results.
  • Choose from an automated data preparation procedure for fast results or select other methods to prepare more challenging datasets.
  • Identify suspicious or invalid cases, variables and data values.
  • View patterns of missing data, summarize variable distributions and more accurately work with algorithms designed for nominal attributes.
• Bootstrapping: The IBM® SPSS® Bootstrapping module makes bootstrapping, a technique for testing model stability, simpler for you to use.
  • Estimates the sampling distribution of an estimator by resampling with replacement from your original sample.
  • Estimates standard errors and confidence intervals for a population parameter such as the mean, median, proportion, odds ratio, correlation coefficient, regression coefficient and more.
  • Controls the number of bootstrap samples, sets a random number seed and indicates whether a simple or stratified method is appropriate.
• Advanced Statistics: IBM® SPSS® Advanced Statistics provides a comprehensive suite of univariate and multivariate analysis tools to uncover deeper insights from your data, including:
  • Advanced methods such as general linear models (GLM) and generalized linear models (GENLIN) enable you to analyze both continuous and categorical data with precision.
  • Generalized linear mixed models (GENLINMIXED) are useful for analyzing hierarchical or repeated-measures data, while Cox regression offers robust capabilities for survival analysis and time-to-event modeling.
  • These advanced techniques offer the flexibility and reliability needed to explore complex relationships and deliver reliable and accurate statistical analysis outcomes.
• Regression: IBM® SPSS® Regression enables you to predict categorical outcomes, create regression models, analyze model summaries and apply various nonlinear regression procedures to datasets when studying consumer buying habits, treatment responses, efficacy of diagnostic measures, credit risk analysis and other situations where ordinary regression and data analysis techniques are limiting or inappropriate.
• Custom Tables: IBM® SPSS® Custom Tables enables you to summarize IBM® SPSS® Statistics data and display your analyses as presentation-quality, production-ready tables.
  • It provides analytical capabilities to help you learn from your data and offers advanced features that enable you to build tables people can easily read and interpret.
  • The solution lets you work with output and present survey results using nesting, stacking and multiple response categories.
  • You can also manage missing values and change labels and formats.
• Forecasting: IBM® SPSS® Forecasting provides advanced capabilities that enable both novice and experienced users to develop reliable forecasts using time-series data.
  • Users with less expertise can create sophisticated forecasts that integrate multiple variables, while experienced forecasters can use the software to validate their models.
  • Examples of time-series forecasting include predicting the number of staff required each day for a call center or forecasting the demand for a particular product or service.
• Decision Trees: IBM® SPSS® Decision Trees enables you to identify groups, discover relationships between them and predict future events.
  • It features visual classification and decision trees to help you present categorical results and more clearly explain analysis to non-technical audiences.
  • Create classification models for segmentation, stratification, prediction, data reduction and variable screening.
  • You can create models for interaction identification, category merging and discretizing continuous variables.
• Direct Marketing: IBM® SPSS® Direct Marketing lets you conduct advanced analysis of your customers or contacts to help improve your results.
  • Choose from recency, frequency and monetary value (RFM) analysis, cluster analysis, prospect profiling, postal code analysis, propensity scoring and control package testing.
  • Understand your customers in greater depth, improve marketing campaigns and maximize the return on investment (ROI) of your marketing budget.
  • Use it to launch or test campaigns, increase cross-sell and up-sell revenue or open a store.
• Neural Networks: IBM® SPSS® Neural Networks uses nonlinear data modeling to discover complex relationships and derive greater value from your data.
  • Take advantage of multilayer perceptron (MLP) or radial basis function (RBF) procedures. You can set the conditions—control the training stopping rules and network architecture—or let the procedure choose.
  • Influence the weighting of variables and specify details of the network architecture.
  • Select the type of model training and share results using graphs and charts.
• Complex Samples: IBM® SPSS® Complex Samples software can compute statistics and standard errors from complex sample designs by incorporating the designs into survey analysis.
  • This solution helps you produce a more accurate picture by allowing subpopulation assessments to consider other subpopulations.
  • SPSS Complex Samples offers planning tools such as stratified, clustered or multistage sampling.
  • It's designed to help you reach correct point estimates, predict numerical and categorical outcomes from nonsimple random samples, and account for up to 3 stages when analyzing data from a multistage design.
• Conjoint: The IBM® SPSS® Conjoint module provides conjoint analysis to help you better understand consumer preferences, trade-offs and price sensitivity.
  • It enables you to uncover more information about how customers compare products in the marketplace and measure how individual product attributes affect consumer behavior.
  • The information helps you design, price and market products and services tailored to your customer needs.
• Exact Tests: IBM® SPSS® Exact Tests enables you to analyze rare occurrences in large databases or work with small samples.
  • With over 30 exact tests, you can analyze your data where traditional tests fail. For example, if you have a small number of case variables with a high percentage of responses in one category or have to subset your data into fine breakdowns.
  • The SPSS Exact Tests module operates on Windows, Mac and Linux® platforms and is available as client-only software or as a client or server installation.
• Missing Values: The IBM® SPSS® Missing Values module helps you manage missing values in your data analysis and draw more valid conclusions.
  • Uncover the patterns behind missing data, estimate summary statistics and impute missing values using statistical algorithms.
  • The module helps you build models that account for missing data and remove hidden biases.
  • Survey and market researchers, social scientists, data miners and other professionals rely on IBM SPSS Missing Values to validate their research data.
• Categories: IBM® SPSS® Categories enables you to visualize and explore relationships in your data, helping you predict outcomes based on your statistical analysis. It uses categorical regression techniques to predict the values of nominal, ordinal or numerical outcome variables from a combination of numeric and ordered or unordered categorical predictor variables. The software features statistical procedures such as predictive analysis, statistical learning, perceptual mapping and preference scaling.