Overview

The most powerful software for data restructuring, transformation and statistical analysis...

Get built-in access to a wide variety of data types

Before you start analysis, you have to bring in data - from many types of data sources - into SPSS. And, often, you have to transform that data to get them ready for analysis. Since SPSS has a Windows-based GUI, it's easy to access and manage data.

Database Wizard: use the Database Wizard to easily access massive amounts of data from numerous database sources. You can access databases without having to write SQL code. The Database Wizard guides you through the data access process and generates code in the background. SPSS includes drivers for many ODBC-compliant databases, including Oracle, SQL Server, DB2 UDB, Microsoft Access and Siebel (through an ODBC-compliant driver). With the right drivers, you can connect to any ODBC-compliant database — resulting in minimal data handling using conversion-free/copy-free data access.

SAS Data: SPSS's GET SAS command quickly builds SPSS-format working data files from SAS datasets or SAS transport files (Version 8 or earlier). SPSS automatically adjusts SAS variables and values for SPSS formats.

Text Wizard: read text data — such as survey data, purchased data or data downloaded from the Internet — in a variety of formats using the Text Wizard.

Excel Data: read data stored in Microsoft® Excel files (Version 5.0 or later) directly in SPSS. SPSS also reads columns with mixed data types as valid string variables - without any data loss.

Get your data ready for analysis

Once you've accessed your data, you'll need to prepare them for analysis. SPSS Base includes a variety of techniques to ensure you'll get to analysis faster and with less hassle. With SPSS Base, you can use the following:

Data Editor: gives you a spreadsheet-like system for defining, entering, editing and displaying data

Data preparation tools: get your data ready for analysis. The Define Variable Properties tool enables you to easily set up data dictionary information (such as value labels, variable labels and variable types). A data pass made first enables SPSS to present a list of values and counts of those values so you can add the information in a more intelligent manner. Once dictionary information is set up, you can apply that information using the Copy Data Properties tool. The data dictionary information acts as a "template" so you can apply it to other data files and to other variables within the same file.

Data Restructure Wizard: take a data file that has multiple records per subject and restructure it — so data for each subject are in a single record. No need to set up vectors or loops. This is particularly helpful if you work with transactional data. You can also do the reverse action — that is, take data from a single record and spread it across multiple cases.

Data transformations: work with combined data more reliably by "flipping" responses — so all your data are in the same direction. This is necessary when you want to create multiple-item indices, which require questions to go in the same direction. You may want to create multiple-item indices when working with surveys that ask respondents to give both positively worded and negatively worded responses.

More transformation techniques: SPSS has a variety of other transformation techniques that help get data ready for analysis. These capabilities include:

• Compute new variables using arithmetic, cross-case, data and time, logical, missing-value, random-number, statistical or string functions

• Recode string or numeric value

• Recode values into consecutive integers

• Create conditional transformations using DO IF, ELSE IF, ELSE and END IF statements

• Use programming structures, such as do repeat-end repeat, loop-end loop and vectors

• Count occurrences of values across variables

• Make transformations permanent or temporary

• Execute transformations immediately, batched or on demand

• Cumulative distribution, inverse cumulative distributions and random number generator functions

• Cumulative distribution and random number generator for discrete distribution functions

• Cumulative distribution for non-central distribution

• Density/probability functions for continuous and discrete distributions

• Non-central density/probability functions

• Tail probabilities

• Auxiliary function

A broad choice of statistics for data analysis

SPSS can help you analyse data better because it gives you the statistical depth needed to solve a variety of business and research problems — not just the problem for which you initially purchased the software. SPSS empowers you with a wide range of statistics so you can get the most accurate response for specific data types. Add-on modules and other software give you even more analytical power — and they easily plug into SPSS Base. This means you can add as much analytical capability to your system as you need and work confidently, moving seamlessly from one product to the next.

Statistical highlights for SPSS Base

Linear Regression: explore the relationships between predictors and what you want to predict, for example, predict sales using price and customer type.

Factor Analysis: identify underlying variables or factors that explain correlations within a set of observed variables. For example, use this procedure in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. Factor Analysis has a high degree of flexibility, giving you a number of methods for factor extraction, rotation and factor score computation.

TwoStep Cluster analysis: work with very large datasets using this scalable cluster analysis algorithm. This algorithm can handle both continuous and categorical variables or attributes and requires only one data pass in the procedure. In the first step of the procedure, you pre-cluster the records into many small sub-clusters. Then, you cluster the sub-clusters created in the pre-cluster step into the desired number of clusters. If the desired number of clusters is unknown, TwoStep Cluster analysis automatically finds the proper number of clusters. By using TwoStep Cluster analysis, you can group data so that records within a group are similar. For example, you can apply it to data that describe customer buying habits, gender, age, income, etc. Then, tailor your marketing and product development strategy to each consumer group to increase sales and build brand loyalty.

K-means Cluster Analysis: group data from larger datasets, such as customer mailing lists. This procedure assumes data fall into a known number of clusters. Given this number, the procedure will assign cases to clusters. You can select one of two methods to classify cases — either update cluster centers iteratively or classify only. Save cluster memberships, distance information and final cluster centers. A market researcher, for example, might want to cluster cities into homogeneous groups using K-means Cluster Analysis to find comparable cities to test marketing strategies.

Hierarchical Cluster Analysis: take clusters from a single record and form groups until all clusters are merged. You can choose from over 40 measures of similarity or dissimilarity, standardize data using several methods and cluster cases or variables. You can also analyze raw variables or choose from a variety of standardizing transformations. Generate distance or similarity measures using the proximities procedure. Display statistics at each stage to help you select the best solution. This procedure is recommended for datasets that are smaller in number, for example, focus group lists. A market researcher could use Hierarchical Cluster Analysis to identify types of television shows that attract similar audiences for each show type. The organisation could cluster TV shows into homogenous groups based on viewer characteristics to identify segments for advertising.

SPSS Base gives you:

Descriptive statistics

• Cross-tabulations

• Frequencies

• Descriptives

• Explore

• Descriptive Ratio Statistics

Bivariate statistics

• Means

• t-tests

• ANOVA

• Correlation

  • Bivariate

  • Partial

  • Distances

• Non-parametric tests

Prediction for numerical outcomes

• Linear Regression

Prediction for identifying groups

• Factor Analysis

• TwoStep Cluster Analysis

• K-means Cluster Analysis

• Hierarchical Cluster Analysis

• Discriminant

More statistics for more powerful data analysis
SPSS' add-on modules and stand-alone software offer much more for the data analysis stage, including these statistics:

• Linear Mixed Models in SPSS Advanced Models

• General Linear Models (GLM)in SPSS Advanced Models

• Survival Analysis procedures in SPSS Advanced Models

• Four types of decision tree algorithms in AnswerTree

  • CHAID

  • Exhaustive CHAID

  • Classification and Regression Tree (C&RT)

  • Quest

• Binary Logistic Regression in SPSS Regression Models

• Polytomous Universal Models (PLUM) in SPSS Advanced Models

• Loglinear/Logit Models and General Loglinear Analysis in SPSS Advanced Models

• Probit Models in SPSS Regression Models

• Categorical Regression in SPSS Categories

• Time series analysis in SPSS Trends

• And more including reporting with SPSS Tables

Clearly report your results to the people who can use them

Once your analysis is complete, you usually need to summarize results so non-technical audiences can understand them. Therefore, the goal of the reporting stage is to create easy-to-understand results from your data analysis for decision makers who can quickly understand and act upon your information. That's why SPSS gives you a wide range of ways to report results. With SPSS Base, you get:

REPORT or LIST command: if you want to report cases in your SPSS file, you can use SPSS Base and the REPORT or LIST command.

A variety of charts and graphs: SPSS includes a number of graphical features and chart types so you can provide visuals for your results. A variety of chart types means you can display results using the format you want. Graphing features, such as the ability to rotate charts in real time to gain a multidimensional understanding of charts, help your audience understand results. While, other features, such as templates allowing you to save selected characteristics of a chart and apply them to others automatically, make it easy for you to prepare reports.

Report OLAP cubes: see key findings and explore details in interactive output using SPSS' award-winning report OLAP cubes. Report cubes are interactive tables that enable you to slice, dice and drill down into your data for data exploration. SPSS' report OLAP cubes are easier to use than OLAP cubes found in other software. You can set up SPSS' OLAP cubes yourself — there's no need to involve your IT department. SPSS report OLAP cubes work right from your SPSS data files to include value and variable labels. Also, SPSS can take millions of rows and aggregate them in an OLAP cube to make them meaningful in seconds — empowering you to easily examine all kinds of data.

Export to Microsoft® Excel: easily export pivot tables, including the current view or all layers, to a current version of Excel. This empowers you to put your SPSS tables (including those created in the SPSS Tables® add-on module) on the same sheet or on separate sheets in the same Excel workbook file.

Export to Microsoft® Word: easily export SPSS output into Word for final report creation. Pivot tables can be converted to Word tables with all formatting saved, and all graphics are converted into static pictures.

SPSS system requirements

SPSS

• Microsoft Windows 98, Me, XP, NT 4.0 or 2000

• Pentium-class processor

• 100MB hard drive space (Base only)

• 64MB RAM minimum

• SVGA monitor

SPSS Server

System requirements vary according to platform.