IBM® SPSS® Statistics Base Edition provides capabilities that support the entire analytics process including data preparation, descriptive statistics, linear regression, visual graphing and reporting.
You can access multiple data formats without any data processing size limits. Advanced data preparation capabilities help eliminate labor-intensive manual tasks. Use over 30 analytical procedures such as bivariate statistics procedures, factor and cluster analysis and bootstrapping. You can also extend your capabilities with R or Python. Explore the features in detail below.
Prepare, analyze and understand data
Summarize and standardize scale variables using the descriptives procedure. Study relationships between scale and categorical variables using procedures such as means, summarize, and OLAP cubes.
Model the value of a dependent variable based on its relationship with predictor variables using procedures such as linear, ordinal or partial least square regression.
Use advanced techniques to streamline the data preparation stage — delivering faster analysis and accurate conclusions.
Measure how variables are related to each other using procedures like bivariate correlations or partial correlations.
Reveal natural groupings or clusters within a data set that would otherwise not be apparent using exploratory tools such as two-step, hierarchical or k-means cluster analysis procedures.
Derive robust estimates of standard errors and confidence intervals for estimates including mean, median, correlation coefficient and regression coefficient.
Graphs and charts
Use Chart Builder to build drag-and-drop chart types from a predefined gallery onto the canvas. Use tools such as ROC analysis to assess the accuracy of model predictions by plotting sensitivity versus (1-specificity) of a classification test.
Save the results of your analysis in multiple formats including HTML, text, RTF, PDF, Microsoft Word, Excel and PowerPoint (97 or later), and PDF. Quickly export charts to one of the supported graphics formats.
Evaluate performance of classification schemes in which there is one variable with two categories by which subjects are classified.
Learn how to use factor analysis for data reduction and structure detection.
Test the hypothesis that the means of two groups are not significantly different.
Base Edition features
- Automated data preparation — enhanced model viewer for automated data preparation
- Validate data — streamline the process of validating data before analyzing it
- Anomaly detection — identify unusual cases in a multivariate setting
- Optimal binning
- Sampling and pooling
- Descriptive procedures that can be bootstrapped (correlations/nonparametric correlations, crosstabs, descriptives, examine, frequencies, means, partial correlations, T tests)
Data access and management
- Compare two data files for compatibility
- Data preparation features: Define Variable Properties tool; Copy Data Properties tool, Visual Bander, Identify Duplicate Cases; Date/Time wizard
- Data restructure wizard (single record to multiple records, multiple records to single record)
- Direct Excel data access, easier importing from Excel and CSV
- Export data to SAS and current versions of Excel, export/insert to database wizard
- Import data from IBM Cognos® Business Intelligence, import/export to/from dimensions, import Stata files (until V14)
- Long variable names, longer value labels
- Multiple datasets can be run in one SPSS session
- ODBC Capture — DataDirect drivers, OLE DB data access
- Password protection, SAS 7/8/9 data files (including compressed files)
- Text wizard, Unicode support, very long text strings
- Auto and cross-correlation graphs
- Basic graphs
- Mapping (geospatial analysis)
- Chart gallery
- Chart options
- Chart Builder UI for commonly used charts
- Charts for multiple response variables
- Graphics Production Language for custom charts
- Interactive graphs — scriptable
- Overlay and dual Y charts
- Paneled charts
- ROC analysis
- Time-series charts
- Case summaries
- Style output
- Conditional formatting
- Export charts as Microsoft Graphic Object
- Export model as XML to SmartScore
- Export to PDF
- Export to Word/Excel/PowerPoint
- HTML output
Improved performance for large pivot tables
- OLAP cubes/pivot tables
- Output management system
- Output scripting
- Reports summaries in rows and columns
- Search and replace
- Smart devices (tablets and phones)
- Table to graph conversion
- Web reports
- Application examples
- Statistics coach
Data editor enhancements
- Custom attributes for user-defined metadata
- Spell checker
- Splitter controls
- Variable sets for wide data
- Variable icons
- Custom UI builder enhancements (work seamlessly with Python and R and can be used in IBM SPSS® Modeler)
- New Extensions hub
- Custom dialog builder for Extensions
- Flow control or syntax jobs
- Partial least squares regression
- Python, .NET and Java for front-end scripting
- SPSS equivalent of the SAS DATA STEP
- Support for R algorithms and graphics
- User-defined procedures
- ANOVA (in syntax only), one-way ANOVA
- Cluster, two-step cluster: categorical and continuous data/large data sets
- Correlate — bivariate, partial, distances
- Define variable sets, descriptive ratio statistics (PVA)
- Descriptive, means, ratio, summarize data
- Enhanced model viewer on two-step cluster and new nonparametrics
- Explore, crosstabs, frequencies
- Factor analysis, discriminant analysis
- Geospatial analytics (STP and GSAR)
- Improved performance for frequencies, crosstabs, descriptives (Statistics Base Server)
- Matrix operations, Monte Carlo simulation
- Nearest neighbor analysis, new nonparametric tests
- Automatic linear models, ordinal regression (PLUM), ordinary least squares regression
- PP plots, QQ plots, rule checking on secondary SPC charts
- Reliability and ALSCAL multidimensional scaling
- ROC curve, compare ROC curves
- T tests: paired samples, independent samples, one-samples
- Power analysis, weighted Kappa