– IBM® SPSS® STATISTICS –

 

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IBM® SPSS® Statistics addresses the entire process – from planning to data collection to analysis, reporting and deployment. With more than a dozen fully integrated modules to choose from, you can choose the specialized capabilities you need to increase revenue, outperform competitors, conduct research and make better decisions.

Start leveraging your data today to identify your best customers, forecast future trends, improve supplier performance - and more - with some of the most widely used options:

Providing fundamental analytical capabilities for a wide variety of business and research questions.

Standard

Adds functionality to address issues of data quality, data complexity, automation and forecasting.

Professional

includes a full range of analytical techniques plus structural equation modeling (SEM), in-depth sampling assessment and testing, and procedures for direct marketing.

premium

Choose your edition and add what you need.

 

With a rich set of capabilities for every stage of the analytical process, you can choose from a broad range of tools, tests and techniques to quickly and confidently perform any type of analysis.

The modules in the IBM® SPSS® Statistics family can either be purchased individually, or bundled to fit your needs:

Base
Advanced Statistics
Custom Tables
Regression
Data Preperation
Missing Values
Categories
Decision Trees
Forecasting
Boots-
trapping
Conjoint
Exact Test
Neutral Networks
Direct Marketing
Complex Samples
Advanced Statistics

Powerful modeling techniques for analyzing complex relationships.

IBM® SPSS® Advanced Statistics provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are frequently applied to gain deeper insights from data used in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

IBM® SPSS® Advanced Statistics provides the following capabilities:

General linear models (GLM)
  • Describe the relationship between a dependent variable and a set of independent variables. Models include linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA).
  • Use flexible design and contrast options to estimate means and variances and to test and predict means.
  • Mix and match categorical and continuous predictors to build models, choosing from many model-building possibilities.
  • Use linear mixed models for greater accuracy when predicting nonlinear outcomes, such as what a customer is likely to buy, by taking into account hierarchical and nested data structures.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance and randomized complete blocks design.
Generalized linear models (GENLIN)
  • Provide a unifying framework that includes classical linear models with normally distributed dependent variables, logistic and probit models for binary data, and loglinear models for count data, as well as various other nonstandard regression-type models.
  • Apply many useful general statistical models including ordinal regression, Tweedie regression, Poisson regression, Gamma regression and negative binomial regression.
Linear mixed models/hierarchical linear models (HLM)
  • Model means, variances and covariances in data that display correlation and non-constant variability, such as students nested within classrooms or consumers nested within families.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design.
  • Select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive.
  • Get more accurate results when working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both.
Generalized estimating equations (GEE) procedures
  • Extend generalized linear models to accommodate correlated longitudinal data and clustered data.
  • Model correlations within subjects.
Generalized linear mixed models (GLMM)
  • Access, manage and analyze virtually any kind of data set including survey data, corporate databases or data downloaded from the web.
  • Run the GLMM procedure with ordinal values to build more accurate models when predicting nonlinear outcomes such as whether a customer’s satisfaction level will fall under the low, medium or high category.
Survival analysis procedures
  • Choose from a flexible and comprehensive set of techniques for understanding terminal events such as part failure, death or survival rates.
  • Use Kaplan-Meier estimations to gauge the length of time to an event.
  • Select Cox regression to perform proportional hazard regression with time-to-response or duration response as the dependent variable.
Custom Tables

Analyze your data with custom tables created in less time.

IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret. This software is useful for anyone who creates and updates reports on a regular basis, especially those who work in survey or market research, the social sciences, database or direct marketing and institutional research.

IBM® SPSS® Custom Tables includes capabilities to help you:

Get in-depth analyses
  • Include inferential statistics, also known as significance testing, to highlight opportunities or problem areas.
  • Compare means or proportions for demographic groups, customer segments, time periods or other categorical variables.
  • Identify trends, changes or major differences in your data.
  • Show the results of significance tests directly in IBM® SPSS® Custom Tables output.
  • Select from various summary statistics, including categorical variables, measures of dispersion, multiple response sets, scale variables and custom total summaries for categorical variables.
Preview tables as you build them
  • Drag and drop variables onto the table builder and view them in a preview pane before adding them to your tables.
  • Interact with the variables on your screen and know immediately how your data is structured.
  • Move variables from row to column for precise positioning; add, swap and nest variables; or hide variable labels from within the table preview builder.
  • Collapse large or complex tables for a more concise view and still see your variables.
  • Preview the arrangement of variables, including dimensions, stacking or nesting, as well as the categories of each variable and requested statistics.
Customize table layout and appearance
  • Create totals and subtotals without changing your data file and sort categories within your table without affecting the subtotal calculation.
  • Change variable types, exclude categories, sort categories by any summary statistic and hide the categories that comprise subtotals.
  • Add titles and captions, use table expressions in titles and specify minimum and maximum column widths during table creation.
  • Select from pre-formatted styles found within IBM® SPSS® Statistics Base or create your own styles.
  • Add scripts to automate formatting and other repetitive tasks through IBM® SPSS®Statistics Base.
Make results easily available
  • Create customized tabular reports suitable for a variety of audiences, including those without a technical background.
  • Use syntax and automation to run frequently needed reports in production mode, or to create reports with the same structure.
  • Produce all results as IBM® SPSS® pivot tables that can be easily exported to Microsoft® Word, Excel or HTML with formatting intact.

Analyze your data with custom tables created in less time. IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret.

Regression

Improve the accuracy of predictions with advanced regression procedures.

IBM® SPSS® Regression software enables you to predict categorical outcomes and apply a range of nonlinear regression procedures. You can apply the procedures to business and analysis projects where ordinary regression techniques are limiting or inappropriate—such as studying consumer buying habits, responses to treatments or analyzing credit risk. With IBM® SPSS® Regression software, you can expand the capabilities of IBM® SPSS® Statistics Base for the data analysis stage in the analytical process.

Predict categorical outcomes
  • Using MLR, regress a categorical dependent variable with more than two categories on a set of independent variables. This helps you accurately predict group membership within key groups.
  • Use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor.
  • For a large number of predictors, use Score and Wald methods to help you quickly reach results.
  • Assess your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC).
Easily classify your data
  • Using binary logistic regression, build models in which the dependent variable is dichotomous; for example, buy versus not buy, pay versus default, graduate versus not graduate.
  • Predict the probability of events such as solicitation responses or program participation.
  • Select variables using six types of stepwise methods. This includes forward (select the strongest variables until there are no more significant predictors in the data set) and backward (at each step, remove the least significant predictor in the data set).
  • Set inclusion or exclusion criteria.
Estimate parameters of nonlinear models
  • Estimate nonlinear equations using NLR for unconstrained problems and CNLR for both constrained and unconstrained problems.
  • Using NLR, estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms.
  • With CNLR, use linear and nonlinear constraints on any combination of parameters.
  • Estimate parameters by minimizing any smooth loss function (objective function), and compute bootstrap estimates of parameter standard errors and correlations.
Meet statistical assumptions
  • If the spread of residuals is not constant, use weighted least squares to estimate the model. For example, when predicting stock values, stocks with higher share values fluctuate more than low-value shares.
  • Use two-stage least squares to estimate the dependent variable when the independent variables are correlated with regression error terms. This allows you to control for correlations between predictor variables and error terms.
Evaluate the value of stimuli
  • Use probit analysis to estimate the effects of one or more independent variables on a categorical dependent variable.
  • Evaluate the value of stimuli using a logit or probit transformation of the proportion responding.
Data Preparation

Improve data preparation for more accurate results.

IBM® SPSS® Data Preparation performs advanced techniques that streamline the data preparation stage of the analytical process to deliver faster, more accurate data analysis results. Analysts can choose from a completely automated data preparation procedure for the fastest results, or select from several other methods to help prepare more challenging data sets. With this software, you can easily identify suspicious or invalid cases, variables and data values. You can also view patterns of missing data, summarize variable distributions and more accurately work with algorithms designed for nominal attributes.

IBM® SPSS® Data Preparation helps:

Automate the data preparation process
  • Prepare data in a single step.
  • Detect and correct quality errors and impute missing values.
  • Quickly determine which data to use in your analysis.
  • View easy-to-understand reports with recommendations and visualizations.
Validate data without manual checks
  • Ensure consistency of data validation from project to project.
  • Apply validation rules based on each variable’s measure level (categorical or continuous).
  • Receive reports of invalid cases, rule violation summaries and number of cases affected.
  • Remove or correct suspicious cases at your discretion before analysis.
Prevent outliers from skewing analyses
  • Search for unusual cases based upon deviations from similar cases.
  • Flag outliers by creating a new variable.
  • Examine unusual cases to determine if they should be included in analyses.
Missing Values

Build better models when you estimate missing data.

IBM® SPSS® Missing Values software is used by survey researchers, social scientists, data miners, market researchers and others to validate data. The software allows you to examine data to uncover missing data patterns, then estimate summary statistics and impute missing values using statistical algorithms. With IBM® SPSS® Missing Values software, you can impute your missing data, draw more valid conclusions and remove hidden bias.

Quickly diagnose missing data imputation problems
  • Examine data from different angles using six diagnostic reports.
  • Diagnose missing data using the data patterns report, which provides a case-by-case overview of your data.
  • Determine the extent of missing data and any extreme values for each case.
Replace missing data values with estimates
  • Understand missing patterns in your data set and replace missing values with plausible estimates.
  • Benefit from an automatic imputation model that chooses the most suitable method based on characteristics of your data, or customize your imputation model.
  • Model the individual data sets that are created, using techniques such as linear regression or expectation maximization algorithms, to produce parameter estimates for each.
  • Obtain final parameter estimates by pooling estimates and computing inferential statistics that take into account variation within and between imputations.
Display and analyze patterns
  • Display missing data for all cases and all variables using the data patterns table.
  • Determine differences between missing and non-missing groups for a related variable with the separate t-test table.
  • Assess how much the missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table.
Categories

Predict outcomes and reveal relationships in categorical data.

IBM® SPSS® Categories makes it easy to visualize and explore relationships in your data and predict outcomes based on your findings. Using advanced techniques, such as predictive analysis, statistical learning, perceptual mapping and preference scaling, you can understand which characteristics consumers relate most closely to your product or brand, and learn how they perceive your products in relation to others.

IBM® SPSS® Categories includes advanced analytical techniques to help you:

Easily analyze and interpret multivariate data
  • Use categorical regression procedures to predict the values of a nominal, ordinal or numerical outcome variable from a combination of numeric and (un)ordered categorical predictor variables.
  • Quantify the variables to maximize the Multiple R with optimal scaling techniques.
  • Clearly see relationships in your data using dimension reduction techniques such as perceptual maps and biplots.
  • Gain insight into relationships among more than two variables with summary charts that display similar variables or categories.
Turn qualitative variables into quantitative ones
  • Predict the values of a nominal, ordinal or numerical outcome variable from a combination of categorical predictor variables.
  • Analyze two-way tables that contain some measurement of correspondence between rows and columns, as well as display rows and columns as points in a map. Also analyze multivariate categorical data by allowing the use of more than two variables in your analysis.
  • Use optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels.
  • Compare multiple sets of variables to one another in the same graph after removing the correlation within sets, and visually examine relationships between two sets of objects; for example, consumers and products.
  • Perform multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities).
Graphically display underlying relationships
  • Place the relationships among your variables in a larger frame of reference with optical scaling.
  • Create perceptual maps that graphically display similar variables or categories close to each other for unique insights into relationships between more than two categorical variables.
  • Use biplots and triplots to look at the relationships among cases, variables and categories; for example, to define relationships between products, customers and demographic characteristics.
  • Further visualize relationships among objects using preference scaling, which helps you perform non-metric analyses for ordinal data and obtain more meaningful results.
  • Analyze similarities between objects and incorporate characteristics for objects in the same analysis.
Decision Trees

Easily identify groups and predict outcomes.

IBM® SPSS® Decision Trees helps you better identify groups, discover relationships between them and predict future events. This module features highly visual classification and decision trees that enable you to present categorical results in an intuitive manner, so you can more clearly explain categorical analysis to non-technical audiences. It includes four tree-growing algorithms, giving you the ability to try different types and find the one that best fits your data.

The module provides specialized tree-building techniques for classification within the IBM® SPSS® Statistics environment. The four tree-growing algorithms include:

  • CHAID—a fast, statistical, multi-way tree algorithm that explores data quickly and efficiently, and builds segments and profiles with respect to the desired outcome.
  • Exhaustive CHAID—a modification of CHAID, which examines all possible splits for each predictor.
  • Classification and regression trees (C&RT)—a complete binary tree algorithm that partitions data and produces accurate homogeneous subsets.
  • QUEST—a statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently.
Forecasting

Build expert forecasts—in a flash.

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use IBM® SPSS® Forecasting to validate their models. You get the information you need faster because the software helps you every step of the way.

IBM® SPSS® Forecasting offers:

Advanced statistical techniques
  • Analyze historical data, predict trends faster and deliver information in ways that your organization’s decision-makers can understand and use.
  • Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
  • Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.
  • Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.
  • Write scripts so models can be updated with new data automatically.
Procedures
  • 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.
Bootstrapping

Create more reliable models and generate more accurate results.

IBM® SPSS® Bootstrapping is an efficient way to ensure that analytical models are reliable and will produce accurate results. It can be used to test the stability of analytical models and procedures found throughout the IBM® SPSS® Statistics product family, including descriptive, means, crosstabs, correlations, regression and many others.

IBM® SPSS® Bootstrapping enables you to:

  • Quickly and easily estimate the sampling distribution of an estimator by re-sampling with replacement from the original sample.
  • Create thousands of alternate versions of a data set for a more accurate view of what is likely to exist in the population.
  • Reduce the impact of outliers and anomalies, helping to ensure the stability and reliability of your models.
  • Estimate the standard errors and confidence intervals of a population parameter such as the mean, median, proportion, odds ratio, correlation coefficient, regression coefficient and more.
Conjoint

Understand and measure purchasing decisions.

IBM® SPSS® Conjoint helps market researchers increase their understanding of consumer preferences so they can more effectively design, price and market successful products. It enables them to model the consumer decision-making process so they can design products with the features and attributes most important to their target market.

IBM® SPSS® Conjoint includes procedures that can help researchers:

Design an orthogonal array of product attribute combinations
  • Reduce the number of questions asked while ensuring enough information to perform a full analysis.
  • Generate orthogonal main effects fractional factorial designs; ORTHOPLAN is not limited to two-level factors.
  • Specify a variable list, optional variable labels, a list of values for each variable and optional value labels.
  • Generate holdout cards to test the fitted conjoint model.
  • Specify the desired number of cards for the plan.
Produce and print cards
  • Use the PLANCARDS utility procedure to generate printed cards for use as stimuli by respondents.
  • Specify the variables to be used as factors and the order in which their labels are to appear in the output.
  • Choose listing-file formats and card formats.
  • Display output in pivot tables.
Analyze research data
  • Perform an ordinary least-squares analysis of preference or rating data with the conjoint procedure.
  • Work with the plan file generated by PLANCARDS, or a plan file input by the user using a data list.
  • Work with individual-level rank or rating data.
  • Provide individual-level and aggregate results.
  • Select from three conjoint simulation methods: max utility, Bradley-Terry-Luce (BTL) and logit.
Exact Test

Accurately analyze small data sets or those with rare occurrences.

IBM® SPSS® Exact Tests enables you to use small samples and still feel confident about the results. 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, traditional tests could be incorrect. IBM® SPSS® Exact Tests eliminates this risk.

With IBM® SPSS® Exact Tests you can:

  • Run a test at any time with just the click of a button.
  • Choose from more than 30 exact tests, which cover the entire spectrum of nonparametric and categorical data problems for small or large data sets, contingency tables and on measures of association.
  • Slice and dice your data into breakdowns. You aren’t limited by required expected counts of five or more per cell for correct results.
  • Search for rare occurrences within large data sets.
  • Keep your original design or natural categories—for example, regions, income or age groups—and analyze what you intended to analyze.
Neutral Networks

Find more complex relationships in your data.

IBM® SPSS® Neural Networks software offers nonlinear data modeling procedures that enable you to discover more complex relationships in your data. The software lets you set the conditions under which the network learns. You can control the training stopping rules and network architecture, or let the procedure automatically choose the architecture for you.

With IBM® SPSS® Neural Networks software, you can develop more accurate and effective predictive models.

Mine your data for hidden relationships
  • Choose either MLP or RBF algorithms to map relationships implied by the data. The MLP procedure can find more complex relationships, while the RBF procedure is faster.
  • Benefit from feed-forward architectures, which move data in only one direction, from the input nodes through the hidden layer or layers of nodes to the output nodes.
  • Take advantage of algorithms that operate on a training set of data and then apply that knowledge to the entire data set and to any new data.
Control the process
  • Specify the dependent variables, which may be scale, categorical or a combination of the two.
  • Adjust each procedure by choosing how to partition the data set, which architecture to use and what computation resources to apply to the analysis.
  • Choose whether to display the results in tables or graphs, save optional temporary variables to the active data set, or export models in XML-based file format to score future data.
Combine with other statistical procedures or techniques
  • Confirm neural network results with traditional statistical techniques using IBM® SPSS® Statistics Base.
  • Combine with other statistical procedures to gain clearer insight in a number of areas, including market research, database marketing, financial analysis, operational analysis and health care. In market research, for example, you can create customer profiles and discover customer preferences.
Forecasting

Build expert forecasts—in a flash.

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use IBM® SPSS® Forecasting to validate their models. You get the information you need faster because the software helps you every step of the way.

IBM® SPSS® Forecasting offers:

Advanced statistical techniques
  • Analyze historical data, predict trends faster and deliver information in ways that your organization’s decision-makers can understand and use.
  • Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
  • Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.
  • Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.
  • Write scripts so models can be updated with new data automatically.
Procedures
  • 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.
Direct Marketing

Easily identify the right customers and improve campaign results.

IBM® SPSS® Direct Marketing helps you understand your customers in greater depth, improve your marketing campaigns and maximize the ROI of your marketing budget. Conduct sophisticated analyses of your customers or contacts easily – and with a high level of confidence in your results. Choose from recency, frequency and monetary value (RFM) analysis, cluster analysis, prospect profiling, postal code analysis, propensity scoring and control package testing.

IBM® SPSS® Direct Marketing enables database and direct marketers to:

  • Identify which customers are likely to respond to specific promotional offers.
  • Develop a marketing strategy for each customer group.
  • Compare the effectiveness of direct mail campaigns.
  • Boost profits and reduce costs by mailing only to those customers most likely to respond.
  • Identify by postal code the responses to your campaigns.
  • Connect to Salesforce.com to extract customer information, collect details on opportunities and perform analyses.
Complex Samples

Analyze statistical data and interpret survey results from complex samples.

IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. IBM® SPSS® Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

Incorporate sample design into survey analysis
  • Increase the precision of your sample or ensure a representative sample from key groups.
  • Select clusters or groups of sampling units to make your surveys more cost-effective.
  • Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
  • Publish public-use data sets that include your sampling and analysis plans.
  • Use published plans as a template in order to save decisions made when creating the plan.
  • Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
  • Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests
  • Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
  • Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
  • Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
  • Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
  • Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
  • When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
  • Use the IBM® SPSS® Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.
Base

Analyze statistical data and interpret survey results from complex samples.

IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. IBM® SPSS® Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

Incorporate sample design into survey analysis
  • Increase the precision of your sample or ensure a representative sample from key groups.
  • Select clusters or groups of sampling units to make your surveys more cost-effective.
  • Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
  • Publish public-use data sets that include your sampling and analysis plans.
  • Use published plans as a template in order to save decisions made when creating the plan.
  • Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
  • Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests
  • Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
  • Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
  • Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
  • Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
  • Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
  • When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
  • Use the IBM® SPSS® Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.

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Standard

– Standard –

Fundamental analytical capabilities

for a wide variety of business and research questions.

 

The IBM® SPSS® Statistics Standard Edition offers the core statistical procedures business managers and analysts need to address fundamental business and research questions. This software provides tools that allow users to quickly view data, formulate hypotheses for additional testing, and carry out procedures to clarify relationships between variables, create clusters, identify trends and make predictions.

 

The IBM® SPSS® Statistics Standard edition includes the following key capabilities:

 

  • Linear models offer a variety of regression and advanced statistical procedures   designed to fit the inherent characteristics of data describing complex relationships.
  • Nonlinear models provide the ability to apply more sophisticated models to data.
  • Simulation capabilities help analysts automatically model many possible outcomes when inputs are uncertain, improving risk analysis and decision making.
  • Customized tables enable users to easily understand their data and quickly summarize results in different styles for different audiences.
Base
Advanced Statistics
Custom Tables
Regression
Advanced Statistics

Powerful modeling techniques for analyzing complex relationships.

IBM® SPSS® Advanced Statistics provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are frequently applied to gain deeper insights from data used in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

IBM® SPSS® Advanced Statistics provides the following capabilities:

General linear models (GLM)
  • Describe the relationship between a dependent variable and a set of independent variables. Models include linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA).
  • Use flexible design and contrast options to estimate means and variances and to test and predict means.
  • Mix and match categorical and continuous predictors to build models, choosing from many model-building possibilities.
  • Use linear mixed models for greater accuracy when predicting nonlinear outcomes, such as what a customer is likely to buy, by taking into account hierarchical and nested data structures.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance and randomized complete blocks design.
Generalized linear models (GENLIN)
  • Provide a unifying framework that includes classical linear models with normally distributed dependent variables, logistic and probit models for binary data, and loglinear models for count data, as well as various other nonstandard regression-type models.
  • Apply many useful general statistical models including ordinal regression, Tweedie regression, Poisson regression, Gamma regression and negative binomial regression.
Linear mixed models/hierarchical linear models (HLM)
  • Model means, variances and covariances in data that display correlation and non-constant variability, such as students nested within classrooms or consumers nested within families.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design.
  • Select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive.
  • Get more accurate results when working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both.
Generalized estimating equations (GEE) procedures
  • Extend generalized linear models to accommodate correlated longitudinal data and clustered data.
  • Model correlations within subjects.
Generalized linear mixed models (GLMM)
  • Access, manage and analyze virtually any kind of data set including survey data, corporate databases or data downloaded from the web.
  • Run the GLMM procedure with ordinal values to build more accurate models when predicting nonlinear outcomes such as whether a customer’s satisfaction level will fall under the low, medium or high category.
Survival analysis procedures
  • Choose from a flexible and comprehensive set of techniques for understanding terminal events such as part failure, death or survival rates.
  • Use Kaplan-Meier estimations to gauge the length of time to an event.
  • Select Cox regression to perform proportional hazard regression with time-to-response or duration response as the dependent variable.
Custom Tables

Analyze your data with custom tables created in less time.

IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret. This software is useful for anyone who creates and updates reports on a regular basis, especially those who work in survey or market research, the social sciences, database or direct marketing and institutional research.

IBM® SPSS® Custom Tables includes capabilities to help you:

Get in-depth analyses
  • Include inferential statistics, also known as significance testing, to highlight opportunities or problem areas.
  • Compare means or proportions for demographic groups, customer segments, time periods or other categorical variables.
  • Identify trends, changes or major differences in your data.
  • Show the results of significance tests directly in IBM® SPSS® Custom Tables output.
  • Select from various summary statistics, including categorical variables, measures of dispersion, multiple response sets, scale variables and custom total summaries for categorical variables.
Preview tables as you build them
  • Drag and drop variables onto the table builder and view them in a preview pane before adding them to your tables.
  • Interact with the variables on your screen and know immediately how your data is structured.
  • Move variables from row to column for precise positioning; add, swap and nest variables; or hide variable labels from within the table preview builder.
  • Collapse large or complex tables for a more concise view and still see your variables.
  • Preview the arrangement of variables, including dimensions, stacking or nesting, as well as the categories of each variable and requested statistics.
Customize table layout and appearance
  • Create totals and subtotals without changing your data file and sort categories within your table without affecting the subtotal calculation.
  • Change variable types, exclude categories, sort categories by any summary statistic and hide the categories that comprise subtotals.
  • Add titles and captions, use table expressions in titles and specify minimum and maximum column widths during table creation.
  • Select from pre-formatted styles found within IBM® SPSS® Statistics Base or create your own styles.
  • Add scripts to automate formatting and other repetitive tasks through IBM® SPSS®Statistics Base.
Make results easily available
  • Create customized tabular reports suitable for a variety of audiences, including those without a technical background.
  • Use syntax and automation to run frequently needed reports in production mode, or to create reports with the same structure.
  • Produce all results as IBM® SPSS® pivot tables that can be easily exported to Microsoft® Word, Excel or HTML with formatting intact.

Analyze your data with custom tables created in less time. IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret.

Regression

Improve the accuracy of predictions with advanced regression procedures.

IBM® SPSS® Regression software enables you to predict categorical outcomes and apply a range of nonlinear regression procedures. You can apply the procedures to business and analysis projects where ordinary regression techniques are limiting or inappropriate—such as studying consumer buying habits, responses to treatments or analyzing credit risk. With IBM® SPSS® Regression software, you can expand the capabilities of IBM® SPSS® Statistics Base for the data analysis stage in the analytical process.

Predict categorical outcomes
  • Using MLR, regress a categorical dependent variable with more than two categories on a set of independent variables. This helps you accurately predict group membership within key groups.
  • Use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor.
  • For a large number of predictors, use Score and Wald methods to help you quickly reach results.
  • Assess your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC).
Easily classify your data
  • Using binary logistic regression, build models in which the dependent variable is dichotomous; for example, buy versus not buy, pay versus default, graduate versus not graduate.
  • Predict the probability of events such as solicitation responses or program participation.
  • Select variables using six types of stepwise methods. This includes forward (select the strongest variables until there are no more significant predictors in the data set) and backward (at each step, remove the least significant predictor in the data set).
  • Set inclusion or exclusion criteria.
Estimate parameters of nonlinear models
  • Estimate nonlinear equations using NLR for unconstrained problems and CNLR for both constrained and unconstrained problems.
  • Using NLR, estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms.
  • With CNLR, use linear and nonlinear constraints on any combination of parameters.
  • Estimate parameters by minimizing any smooth loss function (objective function), and compute bootstrap estimates of parameter standard errors and correlations.
Meet statistical assumptions
  • If the spread of residuals is not constant, use weighted least squares to estimate the model. For example, when predicting stock values, stocks with higher share values fluctuate more than low-value shares.
  • Use two-stage least squares to estimate the dependent variable when the independent variables are correlated with regression error terms. This allows you to control for correlations between predictor variables and error terms.
Evaluate the value of stimuli
  • Use probit analysis to estimate the effects of one or more independent variables on a categorical dependent variable.
  • Evaluate the value of stimuli using a logit or probit transformation of the proportion responding.
Base

Analyze statistical data and interpret survey results from complex samples.

IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. IBM® SPSS® Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

Incorporate sample design into survey analysis
  • Increase the precision of your sample or ensure a representative sample from key groups.
  • Select clusters or groups of sampling units to make your surveys more cost-effective.
  • Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
  • Publish public-use data sets that include your sampling and analysis plans.
  • Use published plans as a template in order to save decisions made when creating the plan.
  • Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
  • Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests
  • Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
  • Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
  • Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
  • Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
  • Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
  • When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
  • Use the IBM® SPSS® Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.

professional

– Professional –

Tools to adress the challenges of the entire analytic life circle.

 

The IBM® SPSS® Statistics Professional Edition goes beyond the core statistical capabilities offered in the Standard Edition to address issues of data quality, data complexity, automation and forecasting. It is designed for users who perform many types of in-depth and non-standard analyses and who need to save time by automating data preparation tasks.

 

The IBM® SPSS® Statistics Professional edition includes the following key capabilities:

 

  • Linear models offer a variety of regression and advanced statistical procedures designed to fit the inherent characteristics of data describing complex relationships.
  • Nonlinear models provide the ability to apply more sophisticated models to data.
  • Simulation capabilities help analysts automatically model many possible outcomes when inputs are uncertain, improving risk analysis and decision making.
  • Customized tables enable users to easily understand their data and quickly summarize results in different styles for different audiences.
  • Data preparation streamlines the data preparation stage of the analytical process.
  • Data validity and missing values increase the chance of receiving statistically significant results.
  • Decision trees make it easier to identify groups, discover relationships between groups and predict future events.
  • Forecasting features enable you to analyze historical data and predict trends faster.
Base
Advanced Statistics
Custom Tables
Regression
Data Preperation
Missing Values
Categories
Decision Trees
Forecasting
Advanced Statistics

Powerful modeling techniques for analyzing complex relationships.

IBM® SPSS® Advanced Statistics provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are frequently applied to gain deeper insights from data used in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

IBM® SPSS® Advanced Statistics provides the following capabilities:

General linear models (GLM)
  • Describe the relationship between a dependent variable and a set of independent variables. Models include linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA).
  • Use flexible design and contrast options to estimate means and variances and to test and predict means.
  • Mix and match categorical and continuous predictors to build models, choosing from many model-building possibilities.
  • Use linear mixed models for greater accuracy when predicting nonlinear outcomes, such as what a customer is likely to buy, by taking into account hierarchical and nested data structures.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance and randomized complete blocks design.
Generalized linear models (GENLIN)
  • Provide a unifying framework that includes classical linear models with normally distributed dependent variables, logistic and probit models for binary data, and loglinear models for count data, as well as various other nonstandard regression-type models.
  • Apply many useful general statistical models including ordinal regression, Tweedie regression, Poisson regression, Gamma regression and negative binomial regression.
Linear mixed models/hierarchical linear models (HLM)
  • Model means, variances and covariances in data that display correlation and non-constant variability, such as students nested within classrooms or consumers nested within families.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design.
  • Select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive.
  • Get more accurate results when working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both.
Generalized estimating equations (GEE) procedures
  • Extend generalized linear models to accommodate correlated longitudinal data and clustered data.
  • Model correlations within subjects.
Generalized linear mixed models (GLMM)
  • Access, manage and analyze virtually any kind of data set including survey data, corporate databases or data downloaded from the web.
  • Run the GLMM procedure with ordinal values to build more accurate models when predicting nonlinear outcomes such as whether a customer’s satisfaction level will fall under the low, medium or high category.
Survival analysis procedures
  • Choose from a flexible and comprehensive set of techniques for understanding terminal events such as part failure, death or survival rates.
  • Use Kaplan-Meier estimations to gauge the length of time to an event.
  • Select Cox regression to perform proportional hazard regression with time-to-response or duration response as the dependent variable.
Custom Tables

Analyze your data with custom tables created in less time.

IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret. This software is useful for anyone who creates and updates reports on a regular basis, especially those who work in survey or market research, the social sciences, database or direct marketing and institutional research.

IBM® SPSS® Custom Tables includes capabilities to help you:

Get in-depth analyses
  • Include inferential statistics, also known as significance testing, to highlight opportunities or problem areas.
  • Compare means or proportions for demographic groups, customer segments, time periods or other categorical variables.
  • Identify trends, changes or major differences in your data.
  • Show the results of significance tests directly in IBM® SPSS® Custom Tables output.
  • Select from various summary statistics, including categorical variables, measures of dispersion, multiple response sets, scale variables and custom total summaries for categorical variables.
Preview tables as you build them
  • Drag and drop variables onto the table builder and view them in a preview pane before adding them to your tables.
  • Interact with the variables on your screen and know immediately how your data is structured.
  • Move variables from row to column for precise positioning; add, swap and nest variables; or hide variable labels from within the table preview builder.
  • Collapse large or complex tables for a more concise view and still see your variables.
  • Preview the arrangement of variables, including dimensions, stacking or nesting, as well as the categories of each variable and requested statistics.
Customize table layout and appearance
  • Create totals and subtotals without changing your data file and sort categories within your table without affecting the subtotal calculation.
  • Change variable types, exclude categories, sort categories by any summary statistic and hide the categories that comprise subtotals.
  • Add titles and captions, use table expressions in titles and specify minimum and maximum column widths during table creation.
  • Select from pre-formatted styles found within IBM® SPSS® Statistics Base or create your own styles.
  • Add scripts to automate formatting and other repetitive tasks through IBM® SPSS®Statistics Base.
Make results easily available
  • Create customized tabular reports suitable for a variety of audiences, including those without a technical background.
  • Use syntax and automation to run frequently needed reports in production mode, or to create reports with the same structure.
  • Produce all results as IBM® SPSS® pivot tables that can be easily exported to Microsoft® Word, Excel or HTML with formatting intact.

Analyze your data with custom tables created in less time. IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret.

Regression

Improve the accuracy of predictions with advanced regression procedures.

IBM® SPSS® Regression software enables you to predict categorical outcomes and apply a range of nonlinear regression procedures. You can apply the procedures to business and analysis projects where ordinary regression techniques are limiting or inappropriate—such as studying consumer buying habits, responses to treatments or analyzing credit risk. With IBM® SPSS® Regression software, you can expand the capabilities of IBM® SPSS® Statistics Base for the data analysis stage in the analytical process.

Predict categorical outcomes
  • Using MLR, regress a categorical dependent variable with more than two categories on a set of independent variables. This helps you accurately predict group membership within key groups.
  • Use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor.
  • For a large number of predictors, use Score and Wald methods to help you quickly reach results.
  • Assess your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC).
Easily classify your data
  • Using binary logistic regression, build models in which the dependent variable is dichotomous; for example, buy versus not buy, pay versus default, graduate versus not graduate.
  • Predict the probability of events such as solicitation responses or program participation.
  • Select variables using six types of stepwise methods. This includes forward (select the strongest variables until there are no more significant predictors in the data set) and backward (at each step, remove the least significant predictor in the data set).
  • Set inclusion or exclusion criteria.
Estimate parameters of nonlinear models
  • Estimate nonlinear equations using NLR for unconstrained problems and CNLR for both constrained and unconstrained problems.
  • Using NLR, estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms.
  • With CNLR, use linear and nonlinear constraints on any combination of parameters.
  • Estimate parameters by minimizing any smooth loss function (objective function), and compute bootstrap estimates of parameter standard errors and correlations.
Meet statistical assumptions
  • If the spread of residuals is not constant, use weighted least squares to estimate the model. For example, when predicting stock values, stocks with higher share values fluctuate more than low-value shares.
  • Use two-stage least squares to estimate the dependent variable when the independent variables are correlated with regression error terms. This allows you to control for correlations between predictor variables and error terms.
Evaluate the value of stimuli
  • Use probit analysis to estimate the effects of one or more independent variables on a categorical dependent variable.
  • Evaluate the value of stimuli using a logit or probit transformation of the proportion responding.
Data Preparation

Improve data preparation for more accurate results.

IBM® SPSS® Data Preparation performs advanced techniques that streamline the data preparation stage of the analytical process to deliver faster, more accurate data analysis results. Analysts can choose from a completely automated data preparation procedure for the fastest results, or select from several other methods to help prepare more challenging data sets. With this software, you can easily identify suspicious or invalid cases, variables and data values. You can also view patterns of missing data, summarize variable distributions and more accurately work with algorithms designed for nominal attributes.

IBM® SPSS® Data Preparation helps:

Automate the data preparation process
  • Prepare data in a single step.
  • Detect and correct quality errors and impute missing values.
  • Quickly determine which data to use in your analysis.
  • View easy-to-understand reports with recommendations and visualizations.
Validate data without manual checks
  • Ensure consistency of data validation from project to project.
  • Apply validation rules based on each variable’s measure level (categorical or continuous).
  • Receive reports of invalid cases, rule violation summaries and number of cases affected.
  • Remove or correct suspicious cases at your discretion before analysis.
Prevent outliers from skewing analyses
  • Search for unusual cases based upon deviations from similar cases.
  • Flag outliers by creating a new variable.
  • Examine unusual cases to determine if they should be included in analyses.
Missing Values

Build better models when you estimate missing data.

IBM® SPSS® Missing Values software is used by survey researchers, social scientists, data miners, market researchers and others to validate data. The software allows you to examine data to uncover missing data patterns, then estimate summary statistics and impute missing values using statistical algorithms. With IBM® SPSS® Missing Values software, you can impute your missing data, draw more valid conclusions and remove hidden bias.

Quickly diagnose missing data imputation problems
  • Examine data from different angles using six diagnostic reports.
  • Diagnose missing data using the data patterns report, which provides a case-by-case overview of your data.
  • Determine the extent of missing data and any extreme values for each case.
Replace missing data values with estimates
  • Understand missing patterns in your data set and replace missing values with plausible estimates.
  • Benefit from an automatic imputation model that chooses the most suitable method based on characteristics of your data, or customize your imputation model.
  • Model the individual data sets that are created, using techniques such as linear regression or expectation maximization algorithms, to produce parameter estimates for each.
  • Obtain final parameter estimates by pooling estimates and computing inferential statistics that take into account variation within and between imputations.
Display and analyze patterns
  • Display missing data for all cases and all variables using the data patterns table.
  • Determine differences between missing and non-missing groups for a related variable with the separate t-test table.
  • Assess how much the missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table.
Categories

Predict outcomes and reveal relationships in categorical data.

IBM® SPSS® Categories makes it easy to visualize and explore relationships in your data and predict outcomes based on your findings. Using advanced techniques, such as predictive analysis, statistical learning, perceptual mapping and preference scaling, you can understand which characteristics consumers relate most closely to your product or brand, and learn how they perceive your products in relation to others.

IBM® SPSS® Categories includes advanced analytical techniques to help you:

Easily analyze and interpret multivariate data
  • Use categorical regression procedures to predict the values of a nominal, ordinal or numerical outcome variable from a combination of numeric and (un)ordered categorical predictor variables.
  • Quantify the variables to maximize the Multiple R with optimal scaling techniques.
  • Clearly see relationships in your data using dimension reduction techniques such as perceptual maps and biplots.
  • Gain insight into relationships among more than two variables with summary charts that display similar variables or categories.
Turn qualitative variables into quantitative ones
  • Predict the values of a nominal, ordinal or numerical outcome variable from a combination of categorical predictor variables.
  • Analyze two-way tables that contain some measurement of correspondence between rows and columns, as well as display rows and columns as points in a map. Also analyze multivariate categorical data by allowing the use of more than two variables in your analysis.
  • Use optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels.
  • Compare multiple sets of variables to one another in the same graph after removing the correlation within sets, and visually examine relationships between two sets of objects; for example, consumers and products.
  • Perform multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities).
Graphically display underlying relationships
  • Place the relationships among your variables in a larger frame of reference with optical scaling.
  • Create perceptual maps that graphically display similar variables or categories close to each other for unique insights into relationships between more than two categorical variables.
  • Use biplots and triplots to look at the relationships among cases, variables and categories; for example, to define relationships between products, customers and demographic characteristics.
  • Further visualize relationships among objects using preference scaling, which helps you perform non-metric analyses for ordinal data and obtain more meaningful results.
  • Analyze similarities between objects and incorporate characteristics for objects in the same analysis.
Decision Trees

Easily identify groups and predict outcomes.

IBM® SPSS® Decision Trees helps you better identify groups, discover relationships between them and predict future events. This module features highly visual classification and decision trees that enable you to present categorical results in an intuitive manner, so you can more clearly explain categorical analysis to non-technical audiences. It includes four tree-growing algorithms, giving you the ability to try different types and find the one that best fits your data.

The module provides specialized tree-building techniques for classification within the IBM® SPSS® Statistics environment. The four tree-growing algorithms include:

  • CHAID—a fast, statistical, multi-way tree algorithm that explores data quickly and efficiently, and builds segments and profiles with respect to the desired outcome.
  • Exhaustive CHAID—a modification of CHAID, which examines all possible splits for each predictor.
  • Classification and regression trees (C&RT)—a complete binary tree algorithm that partitions data and produces accurate homogeneous subsets.
  • QUEST—a statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently.
Forecasting

Build expert forecasts—in a flash.

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use IBM® SPSS® Forecasting to validate their models. You get the information you need faster because the software helps you every step of the way.

IBM® SPSS® Forecasting offers:

Advanced statistical techniques
  • Analyze historical data, predict trends faster and deliver information in ways that your organization’s decision-makers can understand and use.
  • Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
  • Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.
  • Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.
  • Write scripts so models can be updated with new data automatically.
Procedures
  • 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.
Base

Analyze statistical data and interpret survey results from complex samples.

IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. IBM® SPSS® Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

Incorporate sample design into survey analysis
  • Increase the precision of your sample or ensure a representative sample from key groups.
  • Select clusters or groups of sampling units to make your surveys more cost-effective.
  • Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
  • Publish public-use data sets that include your sampling and analysis plans.
  • Use published plans as a template in order to save decisions made when creating the plan.
  • Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
  • Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests
  • Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
  • Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
  • Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
  • Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
  • Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
  • When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
  • Use the IBM® SPSS® Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.

Premium

– Premium –

Providing a comprehensive package

for those who need advanced analytics across their organization.

 

The IBM® SPSS® Statistics Professional Edition helps data analysts, planners, forecasters, survey researchers, program evaluators and database marketers – among others – to easily accomplish tasks at every phase of the analytical process. It includes a broad array of fully integrated Statistics capabilities and related products for specialized analytical tasks across the enterprise. The software will improve productivity significantly and help achieve superior results for specific projects and business goals.

 

The IBM® SPSS® Statistics Premium edition includes the following key capabilities:

 

  • Model linear or nonlinear data with regression and advanced statistical procedures designed to fit the inherent characteristics of data describing complex relationships
  • Automatically simulate and model multiple possible outcomes when inputs are uncertain, improving risk analysis and decision making
  • Produce custom tables to quickly summarize and communicate results in different styles to your different audiences
  • Streamline the data preparation stage of your analytical process
  • Increase the chances of identify statistically significant results with features to validate your data and manage missing values
  • Graphically predict outcomes and reveal relationships in numerical and categorical data
  • Easily identify groups, discover relationships between groups and predict future events with decision trees
  • Predict trends faster by analyzing historical data and use the forecasting features
  • Build structural equation models with more accuracy than standard multivariate statistics models using intuitive drag-and-drop functionality
  • Test stability and reliability of models easily with bootstrapping to insure they produce accurate, reliable results
  • Make more statistically valid inferences by incorporating the sample design into survey analysis with advanced sampling assessment and testing
  • Enable marketers to identify the right customers easily and improve campaign results with direct marketing and product decision-making tools
  • Create compelling visualizations of your results in high-end charts and graphs, making it easier share and interact across platforms and smart devices
Base
Advanced Statistics
Custom Tables
Regression
Data Preperation
Missing Values
Categories
Decision Trees
Forecasting
Bootstrapping
Conjoint
Exact Test
Neutral Networks
Direct Marketing
Complex Samples
Advanced Statistics

Powerful modeling techniques for analyzing complex relationships.

IBM® SPSS® Advanced Statistics provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are frequently applied to gain deeper insights from data used in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

IBM® SPSS® Advanced Statistics provides the following capabilities:

General linear models (GLM)
  • Describe the relationship between a dependent variable and a set of independent variables. Models include linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA).
  • Use flexible design and contrast options to estimate means and variances and to test and predict means.
  • Mix and match categorical and continuous predictors to build models, choosing from many model-building possibilities.
  • Use linear mixed models for greater accuracy when predicting nonlinear outcomes, such as what a customer is likely to buy, by taking into account hierarchical and nested data structures.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance and randomized complete blocks design.
Generalized linear models (GENLIN)
  • Provide a unifying framework that includes classical linear models with normally distributed dependent variables, logistic and probit models for binary data, and loglinear models for count data, as well as various other nonstandard regression-type models.
  • Apply many useful general statistical models including ordinal regression, Tweedie regression, Poisson regression, Gamma regression and negative binomial regression.
Linear mixed models/hierarchical linear models (HLM)
  • Model means, variances and covariances in data that display correlation and non-constant variability, such as students nested within classrooms or consumers nested within families.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design.
  • Select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive.
  • Get more accurate results when working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both.
Generalized estimating equations (GEE) procedures
  • Extend generalized linear models to accommodate correlated longitudinal data and clustered data.
  • Model correlations within subjects.
Generalized linear mixed models (GLMM)
  • Access, manage and analyze virtually any kind of data set including survey data, corporate databases or data downloaded from the web.
  • Run the GLMM procedure with ordinal values to build more accurate models when predicting nonlinear outcomes such as whether a customer’s satisfaction level will fall under the low, medium or high category.
Survival analysis procedures
  • Choose from a flexible and comprehensive set of techniques for understanding terminal events such as part failure, death or survival rates.
  • Use Kaplan-Meier estimations to gauge the length of time to an event.
  • Select Cox regression to perform proportional hazard regression with time-to-response or duration response as the dependent variable.
Custom Tables

Analyze your data with custom tables created in less time.

IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret. This software is useful for anyone who creates and updates reports on a regular basis, especially those who work in survey or market research, the social sciences, database or direct marketing and institutional research.

IBM® SPSS® Custom Tables includes capabilities to help you:

Get in-depth analyses
  • Include inferential statistics, also known as significance testing, to highlight opportunities or problem areas.
  • Compare means or proportions for demographic groups, customer segments, time periods or other categorical variables.
  • Identify trends, changes or major differences in your data.
  • Show the results of significance tests directly in IBM® SPSS® Custom Tables output.
  • Select from various summary statistics, including categorical variables, measures of dispersion, multiple response sets, scale variables and custom total summaries for categorical variables.
Preview tables as you build them
  • Drag and drop variables onto the table builder and view them in a preview pane before adding them to your tables.
  • Interact with the variables on your screen and know immediately how your data is structured.
  • Move variables from row to column for precise positioning; add, swap and nest variables; or hide variable labels from within the table preview builder.
  • Collapse large or complex tables for a more concise view and still see your variables.
  • Preview the arrangement of variables, including dimensions, stacking or nesting, as well as the categories of each variable and requested statistics.
Customize table layout and appearance
  • Create totals and subtotals without changing your data file and sort categories within your table without affecting the subtotal calculation.
  • Change variable types, exclude categories, sort categories by any summary statistic and hide the categories that comprise subtotals.
  • Add titles and captions, use table expressions in titles and specify minimum and maximum column widths during table creation.
  • Select from pre-formatted styles found within IBM® SPSS® Statistics Base or create your own styles.
  • Add scripts to automate formatting and other repetitive tasks through IBM® SPSS®Statistics Base.
Make results easily available
  • Create customized tabular reports suitable for a variety of audiences, including those without a technical background.
  • Use syntax and automation to run frequently needed reports in production mode, or to create reports with the same structure.
  • Produce all results as IBM® SPSS® pivot tables that can be easily exported to Microsoft® Word, Excel or HTML with formatting intact.

Analyze your data with custom tables created in less time. IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret.

Regression

Improve the accuracy of predictions with advanced regression procedures.

IBM® SPSS® Regression software enables you to predict categorical outcomes and apply a range of nonlinear regression procedures. You can apply the procedures to business and analysis projects where ordinary regression techniques are limiting or inappropriate—such as studying consumer buying habits, responses to treatments or analyzing credit risk. With IBM® SPSS® Regression software, you can expand the capabilities of IBM® SPSS® Statistics Base for the data analysis stage in the analytical process.

Predict categorical outcomes
  • Using MLR, regress a categorical dependent variable with more than two categories on a set of independent variables. This helps you accurately predict group membership within key groups.
  • Use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor.
  • For a large number of predictors, use Score and Wald methods to help you quickly reach results.
  • Assess your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC).
Easily classify your data
  • Using binary logistic regression, build models in which the dependent variable is dichotomous; for example, buy versus not buy, pay versus default, graduate versus not graduate.
  • Predict the probability of events such as solicitation responses or program participation.
  • Select variables using six types of stepwise methods. This includes forward (select the strongest variables until there are no more significant predictors in the data set) and backward (at each step, remove the least significant predictor in the data set).
  • Set inclusion or exclusion criteria.
Estimate parameters of nonlinear models
  • Estimate nonlinear equations using NLR for unconstrained problems and CNLR for both constrained and unconstrained problems.
  • Using NLR, estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms.
  • With CNLR, use linear and nonlinear constraints on any combination of parameters.
  • Estimate parameters by minimizing any smooth loss function (objective function), and compute bootstrap estimates of parameter standard errors and correlations.
Meet statistical assumptions
  • If the spread of residuals is not constant, use weighted least squares to estimate the model. For example, when predicting stock values, stocks with higher share values fluctuate more than low-value shares.
  • Use two-stage least squares to estimate the dependent variable when the independent variables are correlated with regression error terms. This allows you to control for correlations between predictor variables and error terms.
Evaluate the value of stimuli
  • Use probit analysis to estimate the effects of one or more independent variables on a categorical dependent variable.
  • Evaluate the value of stimuli using a logit or probit transformation of the proportion responding.
Data Preparation

Improve data preparation for more accurate results.

IBM® SPSS® Data Preparation performs advanced techniques that streamline the data preparation stage of the analytical process to deliver faster, more accurate data analysis results. Analysts can choose from a completely automated data preparation procedure for the fastest results, or select from several other methods to help prepare more challenging data sets. With this software, you can easily identify suspicious or invalid cases, variables and data values. You can also view patterns of missing data, summarize variable distributions and more accurately work with algorithms designed for nominal attributes.

IBM® SPSS® Data Preparation helps:

Automate the data preparation process
  • Prepare data in a single step.
  • Detect and correct quality errors and impute missing values.
  • Quickly determine which data to use in your analysis.
  • View easy-to-understand reports with recommendations and visualizations.
Validate data without manual checks
  • Ensure consistency of data validation from project to project.
  • Apply validation rules based on each variable’s measure level (categorical or continuous).
  • Receive reports of invalid cases, rule violation summaries and number of cases affected.
  • Remove or correct suspicious cases at your discretion before analysis.
Prevent outliers from skewing analyses
  • Search for unusual cases based upon deviations from similar cases.
  • Flag outliers by creating a new variable.
  • Examine unusual cases to determine if they should be included in analyses.
Missing Values

Build better models when you estimate missing data.

IBM® SPSS® Missing Values software is used by survey researchers, social scientists, data miners, market researchers and others to validate data. The software allows you to examine data to uncover missing data patterns, then estimate summary statistics and impute missing values using statistical algorithms. With IBM® SPSS® Missing Values software, you can impute your missing data, draw more valid conclusions and remove hidden bias.

Quickly diagnose missing data imputation problems
  • Examine data from different angles using six diagnostic reports.
  • Diagnose missing data using the data patterns report, which provides a case-by-case overview of your data.
  • Determine the extent of missing data and any extreme values for each case.
Replace missing data values with estimates
  • Understand missing patterns in your data set and replace missing values with plausible estimates.
  • Benefit from an automatic imputation model that chooses the most suitable method based on characteristics of your data, or customize your imputation model.
  • Model the individual data sets that are created, using techniques such as linear regression or expectation maximization algorithms, to produce parameter estimates for each.
  • Obtain final parameter estimates by pooling estimates and computing inferential statistics that take into account variation within and between imputations.
Display and analyze patterns
  • Display missing data for all cases and all variables using the data patterns table.
  • Determine differences between missing and non-missing groups for a related variable with the separate t-test table.
  • Assess how much the missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table.
Categories

Predict outcomes and reveal relationships in categorical data.

IBM® SPSS® Categories makes it easy to visualize and explore relationships in your data and predict outcomes based on your findings. Using advanced techniques, such as predictive analysis, statistical learning, perceptual mapping and preference scaling, you can understand which characteristics consumers relate most closely to your product or brand, and learn how they perceive your products in relation to others.

IBM® SPSS® Categories includes advanced analytical techniques to help you:

Easily analyze and interpret multivariate data
  • Use categorical regression procedures to predict the values of a nominal, ordinal or numerical outcome variable from a combination of numeric and (un)ordered categorical predictor variables.
  • Quantify the variables to maximize the Multiple R with optimal scaling techniques.
  • Clearly see relationships in your data using dimension reduction techniques such as perceptual maps and biplots.
  • Gain insight into relationships among more than two variables with summary charts that display similar variables or categories.
Turn qualitative variables into quantitative ones
  • Predict the values of a nominal, ordinal or numerical outcome variable from a combination of categorical predictor variables.
  • Analyze two-way tables that contain some measurement of correspondence between rows and columns, as well as display rows and columns as points in a map. Also analyze multivariate categorical data by allowing the use of more than two variables in your analysis.
  • Use optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels.
  • Compare multiple sets of variables to one another in the same graph after removing the correlation within sets, and visually examine relationships between two sets of objects; for example, consumers and products.
  • Perform multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities).
Graphically display underlying relationships
  • Place the relationships among your variables in a larger frame of reference with optical scaling.
  • Create perceptual maps that graphically display similar variables or categories close to each other for unique insights into relationships between more than two categorical variables.
  • Use biplots and triplots to look at the relationships among cases, variables and categories; for example, to define relationships between products, customers and demographic characteristics.
  • Further visualize relationships among objects using preference scaling, which helps you perform non-metric analyses for ordinal data and obtain more meaningful results.
  • Analyze similarities between objects and incorporate characteristics for objects in the same analysis.
Decision Trees

Easily identify groups and predict outcomes.

IBM® SPSS® Decision Trees helps you better identify groups, discover relationships between them and predict future events. This module features highly visual classification and decision trees that enable you to present categorical results in an intuitive manner, so you can more clearly explain categorical analysis to non-technical audiences. It includes four tree-growing algorithms, giving you the ability to try different types and find the one that best fits your data.

The module provides specialized tree-building techniques for classification within the IBM® SPSS® Statistics environment. The four tree-growing algorithms include:

  • CHAID—a fast, statistical, multi-way tree algorithm that explores data quickly and efficiently, and builds segments and profiles with respect to the desired outcome.
  • Exhaustive CHAID—a modification of CHAID, which examines all possible splits for each predictor.
  • Classification and regression trees (C&RT)—a complete binary tree algorithm that partitions data and produces accurate homogeneous subsets.
  • QUEST—a statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently.
Forecasting

Build expert forecasts—in a flash.

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use IBM® SPSS® Forecasting to validate their models. You get the information you need faster because the software helps you every step of the way.

IBM® SPSS® Forecasting offers:

Advanced statistical techniques
  • Analyze historical data, predict trends faster and deliver information in ways that your organization’s decision-makers can understand and use.
  • Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
  • Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.
  • Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.
  • Write scripts so models can be updated with new data automatically.
Procedures
  • 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.
Bootstrapping

Create more reliable models and generate more accurate results.

IBM® SPSS® Bootstrapping is an efficient way to ensure that analytical models are reliable and will produce accurate results. It can be used to test the stability of analytical models and procedures found throughout the IBM® SPSS® Statistics product family, including descriptive, means, crosstabs, correlations, regression and many others.

IBM® SPSS® Bootstrapping enables you to:

  • Quickly and easily estimate the sampling distribution of an estimator by re-sampling with replacement from the original sample.
  • Create thousands of alternate versions of a data set for a more accurate view of what is likely to exist in the population.
  • Reduce the impact of outliers and anomalies, helping to ensure the stability and reliability of your models.
  • Estimate the standard errors and confidence intervals of a population parameter such as the mean, median, proportion, odds ratio, correlation coefficient, regression coefficient and more.
Conjoint

Understand and measure purchasing decisions.

IBM® SPSS® Conjoint helps market researchers increase their understanding of consumer preferences so they can more effectively design, price and market successful products. It enables them to model the consumer decision-making process so they can design products with the features and attributes most important to their target market.

IBM® SPSS® Conjoint includes procedures that can help researchers:

Design an orthogonal array of product attribute combinations
  • Reduce the number of questions asked while ensuring enough information to perform a full analysis.
  • Generate orthogonal main effects fractional factorial designs; ORTHOPLAN is not limited to two-level factors.
  • Specify a variable list, optional variable labels, a list of values for each variable and optional value labels.
  • Generate holdout cards to test the fitted conjoint model.
  • Specify the desired number of cards for the plan.
Produce and print cards
  • Use the PLANCARDS utility procedure to generate printed cards for use as stimuli by respondents.
  • Specify the variables to be used as factors and the order in which their labels are to appear in the output.
  • Choose listing-file formats and card formats.
  • Display output in pivot tables.
Analyze research data
  • Perform an ordinary least-squares analysis of preference or rating data with the conjoint procedure.
  • Work with the plan file generated by PLANCARDS, or a plan file input by the user using a data list.
  • Work with individual-level rank or rating data.
  • Provide individual-level and aggregate results.
  • Select from three conjoint simulation methods: max utility, Bradley-Terry-Luce (BTL) and logit.
Exact Test

Accurately analyze small data sets or those with rare occurrences.

IBM® SPSS® Exact Tests enables you to use small samples and still feel confident about the results. 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, traditional tests could be incorrect. IBM® SPSS® Exact Tests eliminates this risk.

With IBM® SPSS® Exact Tests you can:

  • Run a test at any time with just the click of a button.
  • Choose from more than 30 exact tests, which cover the entire spectrum of nonparametric and categorical data problems for small or large data sets, contingency tables and on measures of association.
  • Slice and dice your data into breakdowns. You aren’t limited by required expected counts of five or more per cell for correct results.
  • Search for rare occurrences within large data sets.
  • Keep your original design or natural categories—for example, regions, income or age groups—and analyze what you intended to analyze.
Neutral Networks

Find more complex relationships in your data.

IBM® SPSS® Neural Networks software offers nonlinear data modeling procedures that enable you to discover more complex relationships in your data. The software lets you set the conditions under which the network learns. You can control the training stopping rules and network architecture, or let the procedure automatically choose the architecture for you.

With IBM® SPSS® Neural Networks software, you can develop more accurate and effective predictive models.

Mine your data for hidden relationships
  • Choose either MLP or RBF algorithms to map relationships implied by the data. The MLP procedure can find more complex relationships, while the RBF procedure is faster.
  • Benefit from feed-forward architectures, which move data in only one direction, from the input nodes through the hidden layer or layers of nodes to the output nodes.
  • Take advantage of algorithms that operate on a training set of data and then apply that knowledge to the entire data set and to any new data.
Control the process
  • Specify the dependent variables, which may be scale, categorical or a combination of the two.
  • Adjust each procedure by choosing how to partition the data set, which architecture to use and what computation resources to apply to the analysis.
  • Choose whether to display the results in tables or graphs, save optional temporary variables to the active data set, or export models in XML-based file format to score future data.
Combine with other statistical procedures or techniques
  • Confirm neural network results with traditional statistical techniques using IBM® SPSS® Statistics Base.
  • Combine with other statistical procedures to gain clearer insight in a number of areas, including market research, database marketing, financial analysis, operational analysis and health care. In market research, for example, you can create customer profiles and discover customer preferences.
Forecasting

Build expert forecasts—in a flash.

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use IBM® SPSS® Forecasting to validate their models. You get the information you need faster because the software helps you every step of the way.

IBM® SPSS® Forecasting offers:

Advanced statistical techniques
  • Analyze historical data, predict trends faster and deliver information in ways that your organization’s decision-makers can understand and use.
  • Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
  • Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.
  • Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.
  • Write scripts so models can be updated with new data automatically.
Procedures
  • 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.
Direct Marketing

Easily identify the right customers and improve campaign results.

IBM® SPSS® Direct Marketing helps you understand your customers in greater depth, improve your marketing campaigns and maximize the ROI of your marketing budget. Conduct sophisticated analyses of your customers or contacts easily – and with a high level of confidence in your results. Choose from recency, frequency and monetary value (RFM) analysis, cluster analysis, prospect profiling, postal code analysis, propensity scoring and control package testing.

IBM® SPSS® Direct Marketing enables database and direct marketers to:

  • Identify which customers are likely to respond to specific promotional offers.
  • Develop a marketing strategy for each customer group.
  • Compare the effectiveness of direct mail campaigns.
  • Boost profits and reduce costs by mailing only to those customers most likely to respond.
  • Identify by postal code the responses to your campaigns.
  • Connect to Salesforce.com to extract customer information, collect details on opportunities and perform analyses.
Complex Samples

Analyze statistical data and interpret survey results from complex samples.

IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. IBM® SPSS® Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

Incorporate sample design into survey analysis
  • Increase the precision of your sample or ensure a representative sample from key groups.
  • Select clusters or groups of sampling units to make your surveys more cost-effective.
  • Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
  • Publish public-use data sets that include your sampling and analysis plans.
  • Use published plans as a template in order to save decisions made when creating the plan.
  • Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
  • Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests
  • Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
  • Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
  • Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
  • Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
  • Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
  • When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
  • Use the IBM® SPSS® Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.
Base

Analyze statistical data and interpret survey results from complex samples.

IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. IBM® SPSS® Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

Incorporate sample design into survey analysis
  • Increase the precision of your sample or ensure a representative sample from key groups.
  • Select clusters or groups of sampling units to make your surveys more cost-effective.
  • Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
  • Publish public-use data sets that include your sampling and analysis plans.
  • Use published plans as a template in order to save decisions made when creating the plan.
  • Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
  • Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests
  • Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
  • Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
  • Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
  • Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
  • Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
  • When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
  • Use the IBM® SPSS® Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.

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Base

+

Advanced Statistics
Custom Tables
Regression
Data Preperation
Missing Values
Categories
Decision Trees
Forecasting
Bootstrapping
Conjoint
Exact Test
Neutral Networks
Direct Marketing
Complex Samples
Advanced Statistics

Powerful modeling techniques for analyzing complex relationships.

IBM® SPSS® Advanced Statistics provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are frequently applied to gain deeper insights from data used in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

IBM® SPSS® Advanced Statistics provides the following capabilities:

General linear models (GLM)
  • Describe the relationship between a dependent variable and a set of independent variables. Models include linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA).
  • Use flexible design and contrast options to estimate means and variances and to test and predict means.
  • Mix and match categorical and continuous predictors to build models, choosing from many model-building possibilities.
  • Use linear mixed models for greater accuracy when predicting nonlinear outcomes, such as what a customer is likely to buy, by taking into account hierarchical and nested data structures.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance and randomized complete blocks design.
Generalized linear models (GENLIN)
  • Provide a unifying framework that includes classical linear models with normally distributed dependent variables, logistic and probit models for binary data, and loglinear models for count data, as well as various other nonstandard regression-type models.
  • Apply many useful general statistical models including ordinal regression, Tweedie regression, Poisson regression, Gamma regression and negative binomial regression.
Linear mixed models/hierarchical linear models (HLM)
  • Model means, variances and covariances in data that display correlation and non-constant variability, such as students nested within classrooms or consumers nested within families.
  • Formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design.
  • Select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive.
  • Get more accurate results when working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both.
Generalized estimating equations (GEE) procedures
  • Extend generalized linear models to accommodate correlated longitudinal data and clustered data.
  • Model correlations within subjects.
Generalized linear mixed models (GLMM)
  • Access, manage and analyze virtually any kind of data set including survey data, corporate databases or data downloaded from the web.
  • Run the GLMM procedure with ordinal values to build more accurate models when predicting nonlinear outcomes such as whether a customer’s satisfaction level will fall under the low, medium or high category.
Survival analysis procedures
  • Choose from a flexible and comprehensive set of techniques for understanding terminal events such as part failure, death or survival rates.
  • Use Kaplan-Meier estimations to gauge the length of time to an event.
  • Select Cox regression to perform proportional hazard regression with time-to-response or duration response as the dependent variable.
Custom Tables

Analyze your data with custom tables created in less time.

IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret. This software is useful for anyone who creates and updates reports on a regular basis, especially those who work in survey or market research, the social sciences, database or direct marketing and institutional research.

IBM® SPSS® Custom Tables includes capabilities to help you:

Get in-depth analyses
  • Include inferential statistics, also known as significance testing, to highlight opportunities or problem areas.
  • Compare means or proportions for demographic groups, customer segments, time periods or other categorical variables.
  • Identify trends, changes or major differences in your data.
  • Show the results of significance tests directly in IBM® SPSS® Custom Tables output.
  • Select from various summary statistics, including categorical variables, measures of dispersion, multiple response sets, scale variables and custom total summaries for categorical variables.
Preview tables as you build them
  • Drag and drop variables onto the table builder and view them in a preview pane before adding them to your tables.
  • Interact with the variables on your screen and know immediately how your data is structured.
  • Move variables from row to column for precise positioning; add, swap and nest variables; or hide variable labels from within the table preview builder.
  • Collapse large or complex tables for a more concise view and still see your variables.
  • Preview the arrangement of variables, including dimensions, stacking or nesting, as well as the categories of each variable and requested statistics.
Customize table layout and appearance
  • Create totals and subtotals without changing your data file and sort categories within your table without affecting the subtotal calculation.
  • Change variable types, exclude categories, sort categories by any summary statistic and hide the categories that comprise subtotals.
  • Add titles and captions, use table expressions in titles and specify minimum and maximum column widths during table creation.
  • Select from pre-formatted styles found within IBM® SPSS® Statistics Base or create your own styles.
  • Add scripts to automate formatting and other repetitive tasks through IBM® SPSS®Statistics Base.
Make results easily available
  • Create customized tabular reports suitable for a variety of audiences, including those without a technical background.
  • Use syntax and automation to run frequently needed reports in production mode, or to create reports with the same structure.
  • Produce all results as IBM® SPSS® pivot tables that can be easily exported to Microsoft® Word, Excel or HTML with formatting intact.

Analyze your data with custom tables created in less time. IBM® SPSS® Custom Tables makes it easy to summarize IBM® SPSS® Statistics data in different styles for different audiences. It combines analytical capabilities to help you learn from your data with features that allow you to build tables people can easily read and interpret.

Regression

Improve the accuracy of predictions with advanced regression procedures.

IBM® SPSS® Regression software enables you to predict categorical outcomes and apply a range of nonlinear regression procedures. You can apply the procedures to business and analysis projects where ordinary regression techniques are limiting or inappropriate—such as studying consumer buying habits, responses to treatments or analyzing credit risk. With IBM® SPSS® Regression software, you can expand the capabilities of IBM® SPSS® Statistics Base for the data analysis stage in the analytical process.

Predict categorical outcomes
  • Using MLR, regress a categorical dependent variable with more than two categories on a set of independent variables. This helps you accurately predict group membership within key groups.
  • Use stepwise functionality, including forward entry, backward elimination, forward stepwise or backward stepwise, to find the best predictor.
  • For a large number of predictors, use Score and Wald methods to help you quickly reach results.
  • Assess your model fit using Akaike information criterion (AIC) and Bayesian information criterion (BIC).
Easily classify your data
  • Using binary logistic regression, build models in which the dependent variable is dichotomous; for example, buy versus not buy, pay versus default, graduate versus not graduate.
  • Predict the probability of events such as solicitation responses or program participation.
  • Select variables using six types of stepwise methods. This includes forward (select the strongest variables until there are no more significant predictors in the data set) and backward (at each step, remove the least significant predictor in the data set).
  • Set inclusion or exclusion criteria.
Estimate parameters of nonlinear models
  • Estimate nonlinear equations using NLR for unconstrained problems and CNLR for both constrained and unconstrained problems.
  • Using NLR, estimate models with arbitrary relationships between independent and dependent variables using iterative estimation algorithms.
  • With CNLR, use linear and nonlinear constraints on any combination of parameters.
  • Estimate parameters by minimizing any smooth loss function (objective function), and compute bootstrap estimates of parameter standard errors and correlations.
Meet statistical assumptions
  • If the spread of residuals is not constant, use weighted least squares to estimate the model. For example, when predicting stock values, stocks with higher share values fluctuate more than low-value shares.
  • Use two-stage least squares to estimate the dependent variable when the independent variables are correlated with regression error terms. This allows you to control for correlations between predictor variables and error terms.
Evaluate the value of stimuli
  • Use probit analysis to estimate the effects of one or more independent variables on a categorical dependent variable.
  • Evaluate the value of stimuli using a logit or probit transformation of the proportion responding.
Data Preparation

Improve data preparation for more accurate results.

IBM® SPSS® Data Preparation performs advanced techniques that streamline the data preparation stage of the analytical process to deliver faster, more accurate data analysis results. Analysts can choose from a completely automated data preparation procedure for the fastest results, or select from several other methods to help prepare more challenging data sets. With this software, you can easily identify suspicious or invalid cases, variables and data values. You can also view patterns of missing data, summarize variable distributions and more accurately work with algorithms designed for nominal attributes.

IBM® SPSS® Data Preparation helps:

Automate the data preparation process
  • Prepare data in a single step.
  • Detect and correct quality errors and impute missing values.
  • Quickly determine which data to use in your analysis.
  • View easy-to-understand reports with recommendations and visualizations.
Validate data without manual checks
  • Ensure consistency of data validation from project to project.
  • Apply validation rules based on each variable’s measure level (categorical or continuous).
  • Receive reports of invalid cases, rule violation summaries and number of cases affected.
  • Remove or correct suspicious cases at your discretion before analysis.
Prevent outliers from skewing analyses
  • Search for unusual cases based upon deviations from similar cases.
  • Flag outliers by creating a new variable.
  • Examine unusual cases to determine if they should be included in analyses.
Missing Values

Build better models when you estimate missing data.

IBM® SPSS® Missing Values software is used by survey researchers, social scientists, data miners, market researchers and others to validate data. The software allows you to examine data to uncover missing data patterns, then estimate summary statistics and impute missing values using statistical algorithms. With IBM® SPSS® Missing Values software, you can impute your missing data, draw more valid conclusions and remove hidden bias.

Quickly diagnose missing data imputation problems
  • Examine data from different angles using six diagnostic reports.
  • Diagnose missing data using the data patterns report, which provides a case-by-case overview of your data.
  • Determine the extent of missing data and any extreme values for each case.
Replace missing data values with estimates
  • Understand missing patterns in your data set and replace missing values with plausible estimates.
  • Benefit from an automatic imputation model that chooses the most suitable method based on characteristics of your data, or customize your imputation model.
  • Model the individual data sets that are created, using techniques such as linear regression or expectation maximization algorithms, to produce parameter estimates for each.
  • Obtain final parameter estimates by pooling estimates and computing inferential statistics that take into account variation within and between imputations.
Display and analyze patterns
  • Display missing data for all cases and all variables using the data patterns table.
  • Determine differences between missing and non-missing groups for a related variable with the separate t-test table.
  • Assess how much the missing data for one variable relates to the missing data of another variable using the percent mismatch of patterns table.
Categories

Predict outcomes and reveal relationships in categorical data.

IBM® SPSS® Categories makes it easy to visualize and explore relationships in your data and predict outcomes based on your findings. Using advanced techniques, such as predictive analysis, statistical learning, perceptual mapping and preference scaling, you can understand which characteristics consumers relate most closely to your product or brand, and learn how they perceive your products in relation to others.

IBM® SPSS® Categories includes advanced analytical techniques to help you:

Easily analyze and interpret multivariate data
  • Use categorical regression procedures to predict the values of a nominal, ordinal or numerical outcome variable from a combination of numeric and (un)ordered categorical predictor variables.
  • Quantify the variables to maximize the Multiple R with optimal scaling techniques.
  • Clearly see relationships in your data using dimension reduction techniques such as perceptual maps and biplots.
  • Gain insight into relationships among more than two variables with summary charts that display similar variables or categories.
Turn qualitative variables into quantitative ones
  • Predict the values of a nominal, ordinal or numerical outcome variable from a combination of categorical predictor variables.
  • Analyze two-way tables that contain some measurement of correspondence between rows and columns, as well as display rows and columns as points in a map. Also analyze multivariate categorical data by allowing the use of more than two variables in your analysis.
  • Use optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels.
  • Compare multiple sets of variables to one another in the same graph after removing the correlation within sets, and visually examine relationships between two sets of objects; for example, consumers and products.
  • Perform multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities).
Graphically display underlying relationships
  • Place the relationships among your variables in a larger frame of reference with optical scaling.
  • Create perceptual maps that graphically display similar variables or categories close to each other for unique insights into relationships between more than two categorical variables.
  • Use biplots and triplots to look at the relationships among cases, variables and categories; for example, to define relationships between products, customers and demographic characteristics.
  • Further visualize relationships among objects using preference scaling, which helps you perform non-metric analyses for ordinal data and obtain more meaningful results.
  • Analyze similarities between objects and incorporate characteristics for objects in the same analysis.
Decision Trees

Easily identify groups and predict outcomes.

IBM® SPSS® Decision Trees helps you better identify groups, discover relationships between them and predict future events. This module features highly visual classification and decision trees that enable you to present categorical results in an intuitive manner, so you can more clearly explain categorical analysis to non-technical audiences. It includes four tree-growing algorithms, giving you the ability to try different types and find the one that best fits your data.

The module provides specialized tree-building techniques for classification within the IBM® SPSS® Statistics environment. The four tree-growing algorithms include:

  • CHAID—a fast, statistical, multi-way tree algorithm that explores data quickly and efficiently, and builds segments and profiles with respect to the desired outcome.
  • Exhaustive CHAID—a modification of CHAID, which examines all possible splits for each predictor.
  • Classification and regression trees (C&RT)—a complete binary tree algorithm that partitions data and produces accurate homogeneous subsets.
  • QUEST—a statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently.
Forecasting

Build expert forecasts—in a flash.

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use IBM® SPSS® Forecasting to validate their models. You get the information you need faster because the software helps you every step of the way.

IBM® SPSS® Forecasting offers:

Advanced statistical techniques
  • Analyze historical data, predict trends faster and deliver information in ways that your organization’s decision-makers can understand and use.
  • Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
  • Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.
  • Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.
  • Write scripts so models can be updated with new data automatically.
Procedures
  • 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.
Bootstrapping

Create more reliable models and generate more accurate results.

IBM® SPSS® Bootstrapping is an efficient way to ensure that analytical models are reliable and will produce accurate results. It can be used to test the stability of analytical models and procedures found throughout the IBM® SPSS® Statistics product family, including descriptive, means, crosstabs, correlations, regression and many others.

IBM® SPSS® Bootstrapping enables you to:

  • Quickly and easily estimate the sampling distribution of an estimator by re-sampling with replacement from the original sample.
  • Create thousands of alternate versions of a data set for a more accurate view of what is likely to exist in the population.
  • Reduce the impact of outliers and anomalies, helping to ensure the stability and reliability of your models.
  • Estimate the standard errors and confidence intervals of a population parameter such as the mean, median, proportion, odds ratio, correlation coefficient, regression coefficient and more.
Conjoint

Understand and measure purchasing decisions.

IBM® SPSS® Conjoint helps market researchers increase their understanding of consumer preferences so they can more effectively design, price and market successful products. It enables them to model the consumer decision-making process so they can design products with the features and attributes most important to their target market.

IBM® SPSS® Conjoint includes procedures that can help researchers:

Design an orthogonal array of product attribute combinations
  • Reduce the number of questions asked while ensuring enough information to perform a full analysis.
  • Generate orthogonal main effects fractional factorial designs; ORTHOPLAN is not limited to two-level factors.
  • Specify a variable list, optional variable labels, a list of values for each variable and optional value labels.
  • Generate holdout cards to test the fitted conjoint model.
  • Specify the desired number of cards for the plan.
Produce and print cards
  • Use the PLANCARDS utility procedure to generate printed cards for use as stimuli by respondents.
  • Specify the variables to be used as factors and the order in which their labels are to appear in the output.
  • Choose listing-file formats and card formats.
  • Display output in pivot tables.
Analyze research data
  • Perform an ordinary least-squares analysis of preference or rating data with the conjoint procedure.
  • Work with the plan file generated by PLANCARDS, or a plan file input by the user using a data list.
  • Work with individual-level rank or rating data.
  • Provide individual-level and aggregate results.
  • Select from three conjoint simulation methods: max utility, Bradley-Terry-Luce (BTL) and logit.
Exact Test

Accurately analyze small data sets or those with rare occurrences.

IBM® SPSS® Exact Tests enables you to use small samples and still feel confident about the results. 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, traditional tests could be incorrect. IBM® SPSS® Exact Tests eliminates this risk.

With IBM® SPSS® Exact Tests you can:

  • Run a test at any time with just the click of a button.
  • Choose from more than 30 exact tests, which cover the entire spectrum of nonparametric and categorical data problems for small or large data sets, contingency tables and on measures of association.
  • Slice and dice your data into breakdowns. You aren’t limited by required expected counts of five or more per cell for correct results.
  • Search for rare occurrences within large data sets.
  • Keep your original design or natural categories—for example, regions, income or age groups—and analyze what you intended to analyze.
Neutral Networks

Find more complex relationships in your data.

IBM® SPSS® Neural Networks software offers nonlinear data modeling procedures that enable you to discover more complex relationships in your data. The software lets you set the conditions under which the network learns. You can control the training stopping rules and network architecture, or let the procedure automatically choose the architecture for you.

With IBM® SPSS® Neural Networks software, you can develop more accurate and effective predictive models.

Mine your data for hidden relationships
  • Choose either MLP or RBF algorithms to map relationships implied by the data. The MLP procedure can find more complex relationships, while the RBF procedure is faster.
  • Benefit from feed-forward architectures, which move data in only one direction, from the input nodes through the hidden layer or layers of nodes to the output nodes.
  • Take advantage of algorithms that operate on a training set of data and then apply that knowledge to the entire data set and to any new data.
Control the process
  • Specify the dependent variables, which may be scale, categorical or a combination of the two.
  • Adjust each procedure by choosing how to partition the data set, which architecture to use and what computation resources to apply to the analysis.
  • Choose whether to display the results in tables or graphs, save optional temporary variables to the active data set, or export models in XML-based file format to score future data.
Combine with other statistical procedures or techniques
  • Confirm neural network results with traditional statistical techniques using IBM® SPSS® Statistics Base.
  • Combine with other statistical procedures to gain clearer insight in a number of areas, including market research, database marketing, financial analysis, operational analysis and health care. In market research, for example, you can create customer profiles and discover customer preferences.
Forecasting

Build expert forecasts—in a flash.

IBM® SPSS® Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use IBM® SPSS® Forecasting to validate their models. You get the information you need faster because the software helps you every step of the way.

IBM® SPSS® Forecasting offers:

Advanced statistical techniques
  • Analyze historical data, predict trends faster and deliver information in ways that your organization’s decision-makers can understand and use.
  • Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data.
  • Model hundreds of different time series at once, rather than having to run the procedure for one variable at a time.
  • Save models to a central file so forecasts can be updated when data changes, without having to reset parameters or re-estimate models.
  • Write scripts so models can be updated with new data automatically.
Procedures
  • 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.
Direct Marketing

Easily identify the right customers and improve campaign results.

IBM® SPSS® Direct Marketing helps you understand your customers in greater depth, improve your marketing campaigns and maximize the ROI of your marketing budget. Conduct sophisticated analyses of your customers or contacts easily – and with a high level of confidence in your results. Choose from recency, frequency and monetary value (RFM) analysis, cluster analysis, prospect profiling, postal code analysis, propensity scoring and control package testing.

IBM® SPSS® Direct Marketing enables database and direct marketers to:

  • Identify which customers are likely to respond to specific promotional offers.
  • Develop a marketing strategy for each customer group.
  • Compare the effectiveness of direct mail campaigns.
  • Boost profits and reduce costs by mailing only to those customers most likely to respond.
  • Identify by postal code the responses to your campaigns.
  • Connect to Salesforce.com to extract customer information, collect details on opportunities and perform analyses.
Complex Samples

Analyze statistical data and interpret survey results from complex samples.

IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. IBM® SPSS® Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.

Incorporate sample design into survey analysis
  • Increase the precision of your sample or ensure a representative sample from key groups.
  • Select clusters or groups of sampling units to make your surveys more cost-effective.
  • Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
  • Publish public-use data sets that include your sampling and analysis plans.
  • Use published plans as a template in order to save decisions made when creating the plan.
  • Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
  • Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests
  • Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
  • Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
  • Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
  • Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
  • Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
  • When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
  • Use the IBM® SPSS® Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.
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