SPSS Statistics features

The exhaustive list of features as of July 2022, grouped into modules is as follows:

Data access and management

• Compare two data files for compatibility
• Data prep 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

Data preparation

• Automated data preparation—enhanced modelviewer for automated data preparation
• Validate data—streamline the process of validatingdata before analyzing it
• Anomaly detection—identify unusual cases in amultivariate setting
• Optimal binning

Graphs

• 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
• Panelled charts
• ROC analysis
• Time series charts
• Relationship map

Output

• Case summaries
• Style output
• Conditional formatting
• Codebook
• Export charts as Microsoft Graphic Object
• Export model as XML to Smart Score
• Export to PDF
• Export to Word/Excel/PowerPoint
• HTML output

Help features

• Application examples
• Index
• Tutorial
• Extensions
• Search

Data editor enhancements

• Custom attributes for user-defined metadata
• Spell checker
• Splitter controls
• Variable sets for wide data
• Variable icons
• Optimal binning
• 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

Extended programmability

• 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

Statistics

• ANOVA (in syntax only)
• Automatic linear models
• Cluster
• Correlate—bivariate, partial, distances
• Crosstabs–Define variable sets
• Descriptive ratio statistics (PVA)
• Descriptive
• Discriminant analysis
• Enhanced model viewer on two-step cluster and new nonparametric
• Explore
• Factor analysis
• Frequencies
• Geo-spatial analytics (STP and GSAR) (NEW!)
• Improved performance for frequencies, crosstabs, descriptive
• Power Analysis–(Statistics Base Server)
• Matrix operations
• Means
• Monte Carlo simulation
• Nearest neighbour analysis
• New nonparametric tests
• One way ANOVA
• Ordinal regression (PLUM)
• Ordinary least squares regression
• Power Analysis
• PP plots
• QQ plots
• Ratio
• Reliability and ALSCAL multidimensional scaling
• ROC curve
• Compare ROC curves
• Rule checking on secondary SPC charts
• Summarize data
• T tests: paired samples, independent samples, one-samples
• Two-step cluster: categorical and continuous data/large data sets
• Weighted Cohen’s kappa
• Meta-analysis

Multithreaded algorithms

• SORT

Bootstrapping

• Sampling and pooling
• Descriptive procedures that can be bootstrapped:
• Correlations/nonparametric correlations
• Crosstabs
• Descriptives
• Examine
• Frequencies
• Means
• Partial correlations
• T tests

Custom tables

• 35 descriptive statistics
• Drag and drop interface
• Inferential statistics
• Nested tables
• Place totals in any row, column, or layer
• Post computed categories
• Effective base for weighted sample results
• Put multiple variables into the same table
• Significance tests on multiple response variables
• Significance test in custom tables main table
• Significance values for column means and column proportion tests
• Specialized multiple response set tables
• False discovery correction method for multiple comparisons
• Syntax converter

Regression

• Binary logistic regression
• Logit response models
• Multinomial logistic regression
• Nonlinear regression
• Probit response analysis
• Two stage least squares
• Weighted least squares
• Quantile regression

Advanced statistics

• Cox regression
• General linear modelling (GLM):
• General factorial
• Multivariate (MANOVA)
• Repeated measures
• Variance components
• Generalized linear models and generalized estimating equations
• Gamma regression
• Poisson regression
• Negative binomial
• GENLOG for loglinear and logit
• Generalized linear mixed models (GLMM) (ordinal targets included)
• Bayesian statistics
• Hierarchical loglinear models
• Kaplan Meier
• Linear mixed-level models (aka hierarchical linear models)
• Survival
• Variance component estimation

Complex samples (CS)

• CS Cox regression (also multithreaded)
• CS descriptives
• CS general linear models
• CS logistic regression
• CS ordinal regression
• CS selection
• CS tabulate
• Sampling wizard/Analysis Plan wizard

Exact tests

• Cochran’s Q test
• Contingency coefficient
• Cramer’s V
• Fisher’s exact test
• Somers’ D—symmetric and asymmetric
• Friedman test
• Gamma
• Goodman and Kruskal tau
• Jonckheere-Terpstra test
• Kappa
• Kendall’s coefficient of concordance
• Kendall’s tau-b and tau-c
• Kruskal-Wallis test
• Likelihood ratio test
• Linear-by-linear association test
• Mann-Whitney U or Wilcoxon rank-sum W test
• Marginal homogeneity test
• McNemar test
• Median test
• Pearson Chi-square test
• Pearson’s R
• Phi
• Sign test
• Spearman correlation
• Uncertainty coefficient—symmetric or asymmetric
• Wald-Wolfowitz runs test
• Wilcoxon signed-rank test

Categories

• Correspondence analysis (ANACOR)
• Principal components analysis for categorical data (CATPCA; replaces PRINCALS)
• Ridge regression, lasso, elastic net (CATREG)
• CORRESPONDENCE
• Nonlinear canonical correlation (OVERALS)
• Multidimensional scaling for individual differences scaling with constraints (PROXSCAL)
• Preference scaling (PREFSCAL; multidimensional unfolding)
• Multiple correspondence analysis

Missing values

• Data patterns table
• Imputation with means estimation or regression
• Listwise and pairwise statistics
• Missing patterns table
• Multiple imputation of missing data
• Pooling

Conjoint

• Estimate utilities (CONJOINT)
• For conjoint analysis (ORTHOPLAN)
• PLANCARDS

Decision trees

• C&RT
• CHAID
• Exhaustive CHAID
• QUEST

Forecasting

• Auto regressive integrated moving average
• Autoregression
• Expert modeler exponential smoothing methods
• Forecast multiple series (outcomes) at once
• Temporal causal modelling
• Seasonal decomposition
• Spectral analysis

Neural networks

• Multilayer perception
• Radial basis function

Direct marketing

• Cluster analysis
• Contact profiling
• Control package test
• Propensity to purchase
• RFM analysis: recency, frequency, monetary
• Zip code response

 

AMOS (Structural Equation Modelling )

• Bayesian estimation
• Confirmatory factor analysis
• Enter the model into a spreadsheet-like table (no programming)
• Estimation of categorical and censored data
• Latent class analysis
• Non-graphical method of modeling
• Structural equation modeling/path analysis
• Specify path diagram using syntax