Add-On Modules

SPSS® Statistics Base Edition

ALSCAL. Multidimensional scaling (MDS) and multidimensional unfolding (MDU) using an alternating least-squares algorithm.

Cluster. Hierarchical clusters of items based on distance measures of dissimilarity or similarity. The items being clustered are usually cases, although variables can also be clustered.

Codebook. Reports the dictionary information -- such as variable names, variable labels, value labels, missing values -- and summary statistics for all or specified variables and multiple response sets in the active dataset.

Correlations. Pearson correlations with significance levels, univariate statistics, covariances, and cross-product deviations.

Crosstabs. Crosstabulations (contingency tables) and measures of association.

Curvefit. Fits selected curves to a line plot.

Descriptives. Univariate statistics, including the mean, standard deviation, and range.

Discriminant. Classifies cases into one of several mutually exclusive groups based on their values for a set of predictor variables.

Examine. Descriptive statistics, stem-and-leaf plots, histograms, boxplots, normal plots, robust estimates of location, and tests of normality.

Factor. Identifies underlying variables, or factors, that explain the pattern of correlations within a set of observed variables.

Frequencies. Tables of counts and percentages and univariate statistics, including the mean, median, and mode.

Graph. Bar charts, pie charts, line charts, histograms, scatterplots, etc.

KNN. Classifies and predicts cases based upon the values "nearest neighboring" cases.

Linear. Creates a predictive model for a continuous target.

List. Individual case listing.

Means. Group means and related univariate statistics for dependent variables within categories of one or more independent variables.

Mult Response. Frequency tables and crosstabulations for multiple-response data.

Nonparametric. Collection of one-sample, independent samples, and related samples nonparametric tests.

Nonpar Corr. Rank-order correlation coefficients: Spearman’s rho and Kendall’s tau-b, with significance levels.

Npar Tests. Collection of one-sample, independent samples, and related samples nonparametric tests.

OLAP Cubes. Summary statistics for scale variables within categories defined by one or more categorical grouping variables.

Oneway. One-way analysis of variance.

Partial Corr. Partial correlation coefficients between two variables, adjusting for the effects of one or more additional variables.

Plum. Analyzes the relationship between a polytomous ordinal dependent variable and a set of predictors.

Proximities. Measures of similarity, dissimilarity, or distance between pairs of cases or pairs of variables.

Quick Cluster. When the desired number of clusters is known, this procedure groups cases efficiently into clusters.

Ratio Statistics. Descriptive statistics for the ratio between two variables.

Regression. Multiple regression equations and associated statistics and plots.

Reliability. Estimates reliability statistics for the components of multiple-item additive scales.

Report. Individual case listing and group summary statistics.

ROC. Receiver operating characteristic (ROC) curve and an estimate of the area under the curve.

Simplan. Creates a simulation plan for use with the Simrun command.

Simprep Begin-Simprep End. Specifies a block of compute statements and variable definition statements that create a custom model for use with the Simplan command.

Simrun. Runs a simulation based on a simulation plan created by the Simplan command.

Summarize. Individual case listing and group summary statistics.

TTest. One sample, independent samples, and paired samples t tests.

Twostep Cluster. Groups observations into clusters based on a nearness criterion. The procedure uses a hierarchical agglomerative clustering procedure in which individual cases are successively combined to form clusters whose centers are far apart.

Unianova. Regression analysis and analysis of variance for one dependent variable by one or more factors and/or variables.

Xgraph. Creates 3-D bar charts, population pyramids, and dot plots.

Advanced Statistics

Coxreg. Cox proportional hazards regression for analysis of survival times.

Genlin. Generalized Linear Model. Genlin allows you to fit a broad spectrum of “generalized” models in which the distribution of the error term need not be normal and the relationship between the dependent variable and predictors need only be linear through a specified transformation.

Genlinmixed. Generalized linear mixed models extend the linear model so that the target is linearly related to the factors and covariates via a specified link function, the target can have a non-normal distribution, and the observations can be correlated. Generalized linear mixed models cover a wide variety of models, from simple linear regression to complex multilevel models for non-normal longitudinal data.

Genlog. A general procedure for model fitting, hypothesis testing, and parameter estimation for any model that has categorical variables as its major components.

GLM. General Linear Model. A general procedure for analysis of variance and covariance, as well as regression.

Hiloglinear. Fits hierarchical loglinear models to multidimensional contingency tables using an iterative proportional-fitting algorithm.

KM. Kaplan-Meier (product-limit) technique to describe and analyze the length of time to the occurrence of an event.

Mixed. The mixed linear model expands the general linear model used in the GLM procedure in that the data are permitted to exhibit correlation and non-constant variability.

Survival. Actuarial life tables, plots, and related statistics.

Varcomp. Estimates variance components for mixed models.

Regression

Logistic Regression. Regresses a dichotomous dependent variable on a set of independent variables.

Nomreg. Fits a multinomial logit model to a polytomous nominal dependent variable.

NLR, CNLR. Nonlinear regression is used to estimate parameter values and regression statistics for models that are not linear in their parameters.

WLS. Weighted Least Squares. Estimates regression models with different weights for different cases.

2SLS. Two-stage least-squares regression.

Custom Tables

Ctables. Produces tables in one, two, or three dimensions and provides a great deal of flexibility for organizing and displaying the contents.

Decision Trees

Tree. Tree-based classification models.

Categories

Catreg. Categorical regression with optimal scaling using alternating least squares.

CatPCA. Principal components analysis.

Overals. Nonlinear canonical correlation analysis on two or more sets of variables.

Correspondence . Displays the relationships between rows and columns of a two-way table graphically by a scatterplot matrix.

Multiple Correspondence. Quantifies nominal (categorical) data by assigning numerical values to the cases (objects) and categories, such that objects within the same category are close together and objects in different categories are far apart.

Proxscal. Multidimensional scaling of proximity data to find a least-squares representation of the objects in a low-dimensional space.

Complex Samples

CSPlan. Creates a complex sample design or analysis specification.

CSSelect. Selects complex, probability-based samples from a population.

CSDescriptives. Estimates means, sums, and ratios, and computes their standard errors, design effects, confidence intervals, and hypothesis tests.

CSTabulate. Frequency tables and crosstabulations, and associated standard errors, design effects, confidence intervals, and hypothesis tests.

CSGLM. Linear regression analysis, and analysis of variance and covariance.

CSLogistic. Logistic regression analysis on a binary or multinomial dependent variable using the generalized link function.

CSOrdinal. Fits a cumulative odds model to an ordinal dependent variable for data that have been collected according to a complex sampling design.

Neural Networks

MLP. Fits flexible predictive model for one or more target variables, which can be categorical or scale, based upon the values of factors and covariates.

RBF. Fits flexible predictive model for one or more target variables, which can be categorical or scale, based upon the values of factors and covariates. Generally trains faster than MLP at the slight cost of some model flexibility.

Forecasting

Season. Estimates multiplicative or additive seasonal factors.

Spectra. Periodogram and spectral density function estimates for one or more series.

Tsapply. Loads existing time series models from an external file and applies them to data.

Tsmodel. Estimates exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function models) models for time series, and produces forecasts.

Conjoint

Conjoint. Analyzes score or rank data from full-concept conjoint studies.

Orthoplan. Orthogonal main-effects plan for a full-concept conjoint analysis.

Plancards. Full-concept profiles, or cards, from a plan file for conjoint analysis.

Bootstrapping

Bootstrap. Bootstrapping is an alternative to parametric estimates when the assumptions of those methods are in doubt, or where parametric inference is impossible or requires very complicated formulas for the calculation of standard errors.

Missing Values

Multiple Imputation. Performs multiple imputations of missing values. Many other procedures can analyze a multiply-imputed dataset to produce pooled results which are more accurate than the singly-imputed datasets produced by MVA.

MVA. Missing Value Analysis. Describes missing value patterns and estimates (imputes) missing values.

Data Preparation

ADP. Automatically prepares data for modeling.

Detectanomaly. Searches for unusual cases based on deviations from the norms of their cluster groups.

Validatedata. Identifies suspicious and invalid cases, variables, and data values in the active dataset.

Optimal Binning. Discretizes scale “binning input” variables to produce categories that are “optimal” with respect to the relationship of each binning input variable with a specified categorical guide variable.