SPSS Modules

Alphabetical list of SPSS statistical procedures and corresponding modules as of SPSS 16

ALSCAL multidimensional scaling - SPSS Base
Anomaly Detection- SPSS Data Preparation™
ANOVA (analysis of variance) models - simple factorial - SPSS Base
AREG - SPSS Trends™
ARIMA (autoregressive integrated moving average) - SPSS Trends
Bayesian estimation with Markov chain Monte Carlo (MCMC) algorithm - Amos™
Binary logistic regression - SPSS Regression Models™
Bivariate (correlate) - SPSS Base
CAPTCA (categorical principal components analysis) - SPSS Categories™
Case summaries (reports) - SPSS Base
CHAID - SPSS Classification Trees™
Classification & regression trees (C&RT) - SPSS Classification Trees
Cluster - SPSS Base
Complex samples descriptives - SPSS Complex Samples™
Complex samples general linear models - SPSS Complex Samples
Complex samples logistic regression - SPSS Complex Samples
Complex samples ordinal regression - SPSS Complex Samples
Complex samples tabulate - SPSS Complex Samples
Confirmatory factor analysis - Amos
Conjoint - SPSS Conjoint™
Constrained nonlinear regression (CNLR) - SPSS Regression Models
Correspondence analysis - SPSS Categories
Cox regression - SPSS Advanced Models™
Crosstabs (descriptive statistics) - SPSS Base
Curve estimation - SPSS Base
Descriptive statistics - SPSS Base
Descriptive ratio statistics - SPSS Base
Discriminant - SPSS Base
Exact tests - a number of tests and statistics for working with small samples and analyzing rare occurences in large databases - SPSS Exact Tests™
Exhaustive CHAID - SPSS Classification Trees
Explore (descriptive statistics) - SPSS Base
EXSMOOTH (exponential smoothing) - SPSS Trends
Factor - SPSS Base
Fit - SPSS Base
Frequencies (descriptive statistics) - SPSS Base
GEE (generalized estimating equation) - SPSS Advanced Models
GENLOG (loglinear and logit models to count data by means of a generalized linear models approach) - SPSS Advanced Models
GLM (general linear models) - SPSS Advanced Models
GZLM (generalized linear model) - SPSS Advanced Models
HLM (hierarchical linear models) - see linear mixed models
HILOGLINEAR (hierarchical loglinear models) - SPSS Advanced Models
HOMALS - now called multiple correspondence analysis - SPSS Categories
Inferential statistics - SPSS Tables
Kaplan-Meier - SPSS Advanced Models
Linear mixed models - SPSS Advanced Models
Linear regression - SPSS Base
LOGLINEAR (general models of multiway contingency tables) - SPSS Advanced Models
Means (compare means) - SPSS Base
Mixed level models - see linear mixed models
Modeling statistics - SPSS Base
Multinomial logistic regression (MLR) - SPSS Regression Models
Multiple correspondence analysis - previously called HOMALS - SPSS Categories
Multiple response - SPSS Base
Naïve Bayes algorithm - SPSS Server
Nonlinear regression (NLR) - SPSS Regression Models
Nonparametric tests - SPSS Base
OLAP cubes (reports) - SPSS Base
Oneway ANOVA (compare means) - SPSS Base
Ordinal regression (PLUM) - SPSS Base
Orthoplan - SPSS Conjoint
Overals - SPSS Categories
Partial (correlate) - SPSS Base
Plancards - SPSS Categories
Predictor selection algorithm - SPSS Server
Preference scaling (PREFSCAL) - syntax only - SPSS Categories
Probit - SPSS Regression Models
Proximities - SPSS Base
PROXSCAL (multidimensional scaling) - SPSS Categories
QUEST - SPSS Classification Trees
Quick cluster - SPSS Base
Random effects regression - see linear mixed models
Receiver operating characteristic (ROC) analysis - SPSS Base
Reliability - SPSS Base
Report summaries (reports) - SPSS Base
SEASON - SPSS Trends
SEM (structural equation modeling) - Amos
SPECTRA - SPSS Trends
Survival analysis procedures - SPSS Advanced Models
t tests (compare means) - SPSS Base
Two-stage least squares (2SLS) - SPSS Regression Models
TwoStep cluster - SPSS Base
VARCOMP (variance component estimation) - SPSS Advanced Models
Validate Data procedure - SPSS Data Preparation
Weighted least squares (WLS) - SPSS Regression Models

SPSS Base includes:

Descriptive statistics
Crosstabulations
Frequencies
Descriptives
Explore
Descriptive ratio statistics
Bivariate statistics
Means
t tests
ANOVA
Correlation
Bivariate
Partial
Distances
Non-parametric tests
Prediction for numerical outcomes and identifying groups
Factor analysis
TwoStep cluster analysis
K-means cluster analysis
Hierarchical cluster analysis
Discriminant
Linear regression
Ordinal regression
Partial Least Squares regression (requires the SPSS Programmability Extension)
Principal components analysis

SPSS Regression Models includes these procedures:

Multinomial logistic regression (MLR): Predict categorical outcomes with more than two categories
Binary logistic regression: Easily classify your data into two groups
Nonlinear regression (NLR) and constrained nonlinear regression (CNLR): Estimate parameters of nonlinear models
Probit analysis: Evaluate the value of stimuli using a logit or probit transformation of the proportion responding

SPSS Advanced Models

GENLIN include widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, and loglinear models for count data. This procedure also offers many useful statistical models through its very general model formulation.
GEE extend generalized linear models to accommodate correlated longitudinal data and clustered data
General linear models (GLM) procedure:
Linear mixed models, also known as hierarchical linear models (HLM) procedure:

SPSS Categories

Categorical regression (CATREG) predicts the values of a nominal, ordinal, or numerical outcome variable from a combination of categorical predictor variables. Optimal scaling techniques are used to quantify variables.
Correspondence analysis (CORRESPONDENCE) enables you to analyze two-way tables that contain some measurement of correspondence between the rows and columns. You can then visualize these relationships by using biplots and perceptual maps.
Multiple correspondence analysis (MULTIPLE CORRESPONDENCE) is used to analyze multivariate categorical data. It differs from correspondence analysis in that it allows you to use more than two variables in your analysis. With this procedure, all the variables are analyzed at the nominal level (unordered categories).
Categorical principal components analysis (CATPCA) uses optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels. It is similar to multiple correspondence analysis, except that you are able to specify an analysis level on a variable-by-variable basis.
Nonlinear canonical correlation analysis (OVERALS) uses optimal scaling to generalize the canonical correlation analysis procedure so that it can accommodate variables of mixed measurement levels. This type of analysis enables you to compare multiple sets of variables to one another in the same graph, after removing the correlation within sets.
Multidimensional scaling (PROXSCAL) performs multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities). Alternatively, you can compute distances between cases in multivariate data as input to PROXSCAL.
Preference scaling (PREFSCAL) visually examines relationships between variables. Preference scaling performs multidimensional unfolding on two sets of objects in order to find a common quantitative scale.

SPSS Trends

TSMODEL: Use the Expert Modeler to model a set of time-series variables, using either ARIMA or exponential smoothing techniques
TSAPPLY: Apply saved models to new or updated data
SEASON: Estimate multiplicative or additive seasonal factors for periodic time series
SPECTRA: Decompose a time series into its harmonic components, which are sets of regular periodic functions at different wavelengths or periods

Optimal binning - needed for multinomial procedures to converge properly.

SPSS Complex Samples

Accurate statistics when using datasets created by stratified, clustered or multistage sampling
Complex Samples Descriptives (CSDESCRIPTIVES)—
Complex Sample Tabulate (CSTABULATE)—
Complex Samples General Linear Models (CSGLM)—
Complex Ordinals Selection (CSORDINAL)—
Complex Samples Logistic Regression (CSLOGISTIC)—
Complex Samples Cox Regression (CSCOXREG)

SPSS stand-alone products

Amos™
Easily perform structural equation modeling (SEM) to support your research and theories by extending standard multivariate analysis methods. Build attitudinal and behavioral models that more realistically reflect complex relationships, because any numeric variable, whether observed or latent, can be used to predict any other numeric variable.

SPSS Text Analysis for Surveys™
Make short work out of accurately categorizing your open-ended survey responses by combining manual techniques with advanced linguistic processing technologies

SPSS Data Entry™
Options are available for desktop- or network-based data entry; the latter is when networking multiple stations. All SPSS Data Entry products offer complete integration with SPSS for Windows, so you can move from data collection to analysis in a single step.

AnswerTree®
Easily build better profiles, discover segments, and target the right groups using powerful decision-tree algorithms.

SmartViewer® Web Server™
Enable analysts to publish, customize, and distribute interactive SPSS output securely to a web-based audience.

SamplePower®
Save time, effort, and money by identifying the sample size you need to meet your analysis needs before you deploy your survey.

SmartViewer® for Windows
Share report cubes, graphs, and tables electronically while enabling your colleagues and customers to immediately interact with those results on their Windows desktop.

SPSS WebApp Framework
Improve decision making by providing access to web based analytical applications powered by SPSS.

Dimensions™
Collect survey data using products from the Dimensions family. Dimensions software enables you to automatically collect data online, by telephone, through handheld devices, or when using paper forms that you scan. All Dimensions products offer complete integration with SPSS for Windows, enabling you to seamlessly analyze your survey data.