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See new possibilities with predictive analytics
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SPSS 15.0 for Windows®
Powerful new charts and graphs made easy Page 6
SPSS Text Analysis for Surveys™ 2.0
Get more value from open responses Page 10
SPSS 13.0 for Mac® OS X
Major advances in access and data management Page 12
SPSS Advanced Models™ 15.0
Address more statistical modeling problems Page 16
Amos™ 7.0
Amos makes Structural Equation Modeling easy Page 42
And much more!
The marriage of love and analytics:
How eHarmony uses SPSS software to
bring happiness to millions.
SPSS Product Catalog
Your complete data analysis sourcebook
Page 2
Volume 10
Cover Story
ecently, eHarmony, the online dating giant, began extensive use
of SPSS predictive analytical software for scientific research,
brand development, compatibility models, customer satisfaction and
retention, and product research. eHarmony wants to make sure its
researchers and psychologists harness the full power of predictive
analytics for the organization and its customers.
Mining For Love In Myriad Places
By Rick Whiting
Reprinted with permission from InformationWeek
an predictive analysis be used to find
true love? Researchers at eHarmony have
begun using such tools to build the algorithmic
models that power the online matchmaker’s
relationship services.
“SPSS is the lens through which we view our data. The
numerous analytic and data management tools it provides
have enabled our organization to understand important
information in more novel, forward-thinking ways.”
– Steve Carter, eHarmony Senior Director of Research and Development
1.800.253.2575 (U.S. only) Order now!
When dating site eHarmony got its start in
2000, it hired social psychologists who
interviewed married couples to figure out which
characteristics—values, interests, personality
traits—make for a successful relationship. Today
the company has more than 11 million registered
users and claims that, on average, more than
90 eHarmony members marry every day after
being matched on its site. (No mention of how
many matches end in divorce.)
The cyber-Cupid is shooting for higher success
rates. That means identifying more variables
to add to the operational models and more
effectively measuring the variables it already
has. “There have to be tons of things we haven’t
identified yet,” says Steve Carter, eHarmony’s
senior director of research and development.
The software, from SPSS, will be used by
eHarmony for scientific research, brand
development, compatibility models, customer
satisfaction and retention, and product research.
One plan: Begin tracking couples before they
get married to figure out which relationships last
and which ones don’t. The company should think
about sharing those significant findings with the
rest of the world.
Table of Contents
Software Showcase
SPSS 15.0 for Windows
Analyze data using comprehensive
statistical software
SPSS Text Analysis for Surveys 2.0
SPSS 13.0 for Mac OS X
Comprehensive statistical software
for your Mac
Complement SPSS with these products to form a complete
analytical system
SPSS Missing Value Analysis™ 15.0
SPSS Categories™ 15.0
SPSS Regression Models™ 15.0
SPSS Classification Trees™ 15.0
SPSS Tables™ 15.0
SPSS Complex Samples 15.0
SPSS Trends™ 15.0
SPSS Advanced Models™ 15.0
Enter the realm of powerful and
sophisticated analyses
Unleash the full potential of
your multivariate data analysis
Uncover patterns in your data with
powerful tree-growing algorithms
Correctly and easily compute statistics
for complex samples
SPSS Conjoint™ 15.0
Discover what drives your
customers’ purchase decisions
SPSS Data Preparation™ 15.0
Improve data preparation for more
accurate results
SPSS Exact Tests™ 15.0
Reach accurate conclusions with small
samples or rare occurrences
SPSS Maps™ 15.0
Create higher-value data and build better
models when you estimate missing data
Make better predictions with powerful
regression procedures
Build expert time-series forecasts—
in a flash
SPSS Server 15.0
Maximize productivity with SPSS Server
Cover Story
SPSS in the News
The Analytical Process
Amos™ 7.0
Answer Tree® 3.1
SPSS Data Entry ™ 4.0
SamplePower ® 2.0
SPSS SmartViewer ® Web Server ™ 5.0
Create custom tables in no time
Editorial Departments
Chart a course for better decision making
SPSS Stand-alone Products
SPSS Family
Expand your analytical capabilities with SPSS add-on modules
Enhance the analytical value of text responses
Expanded statistical options based on Bayesian estimation
Target the right people more effectively using
intuitive decision trees
Your complete system for collecting and managing
survey research data
Save time, effort, and money by identifying the
sample size you need
Deploy SPSS results with interactive Web reporting
Clementine® Desktop™
mrInterview ™ 3.5 and mrTables™ 3.5
Quickly add the power of data mining to your analysis
Create and conduct surveys with confidence
Mining For Love In Myriad Places
Icon Key
Noteworthy headlines and articles
SPSS end-to-end story
Training is available
SPSS add-on module
Clean up datasets with the REPLACE function
1.800.253.2575 (U.S. only) Order now!
Would you buy a
vehicle and
never take it out
of first gear?
Then consider SPSS training to maximize your software investment.
Your purchase of SPSS software means you’ve bought
SPSS training offers:
a sophisticated analytical tool that has real depth and
power. Wouldn’t you like to be up to speed quickly on
all the ways your purchase can make your life easier
60+ public courses, available at more than
20 locations throughout North America
and your organization more profitable?
Private training fully customized to your
business or organizational needs
On-site training at your location
That’s why SPSS offers training for almost every
One-to-one, in-person training
software product and various applications. We
Live instruction via the Web for individuals
provide a full range of opportunities to get the
quality instruction you require. Whether you prefer
classroom interaction or the convenience of learning
via the Web, you’ll find a format to fit your needs.
and groups
Web-Based Training for on-demand learning
For more information, check your SPSS Training Catalog or visit
Get course descriptions, dates, times, and locations of your
preferred courses, plus money-saving training discounts. Or
call an SPSS representative at 1.800.543.5815.
SPSS In the News
Noteworthy headlines and articles
West Point, Army Select SPSS to Predict
Success Factors and Identify Trends
he United States Military
Academy at West Point
(USMA) has selected SPSS software
to determine the success of cadets at
the academy and as career officers.
“We’ve selected SPSS as our
assessment tool to help us achieve
our overriding mission—to produce
the highest quality career officers.
With SPSS, we’ll better answer
age-old questions with forwardlooking technology,” said West
Point’s Institutional Research and
Analysis Branch Chief Lt. Col.
Michael J. Johnson.
To effectively evaluate cadets’
performance, West Point will build
models with more than 20 years of
personal characteristics collected
from institutional survey data.
Those characteristics will be used
to determine the best predictors
of success as a cadet and as an
Army officer.
SPSS is also used by the U.S.
Army Combat Readiness Center
(CRC) to identify trends within
risk-related data, improving the
Center’s ability to predict, control,
and prevent hazards threatening
U.S. troops. Additionally, the U.S.
Army’s Critical Infrastructure
Assurance Program for Cyber
Threats (CIAP-CT) deploys SPSS
to identify and correlate cyberrelated attack patterns that threaten
critical infrastructures.
SPSS Named As Leader in Customer Data Mining by Leading
Industry Analyst Firm
Gartner, Inc. has positioned SPSS as one of two
company leaders in the business marketplace for
customer data mining. According to Gartner, Leaders
are performing well today, have a clear vision of
market direction, and are actively building competencies
to sustain their leadership position in the market.
“We’re deeply honored to be positioned by Gartner in
the Leaders’ Quadrant for customer data mining,” said
SPSS President and CEO Jack Noonan. “We believe
this is a very significant, independent confirmation of
our technological market position and success. As our
predictive analytics software is driving high ROI with
leading organizations worldwide, SPSS continues to
receive recognition from top industry experts.”
Last year, SPSS was selected by as the
most customer-centric solution provider in marketing
automation. is the world’s largest
industry portal for business executives to learn about
Customer Relationship Management (CRM).
SPSS Software Helping to
“Mushroom” Business for
International Produce Company
SPSS software is enabling Monterey Mushrooms Inc. to more
effectively run all facets of its business from its facilities in the
United States, Canada, and Mexico. Monterey Mushrooms is
the largest grower/shipper and marketer of fresh mushrooms in
the United States.
SPSS software is used in many areas of the business, including
sales reporting, payroll balancing, freight distribution, human
resources, and asset management/general ledger reporting.
See “Mushroom,” continued on page 55
1.800.253.2575 (U.S. only) Order now!
SPSS 15.0 for Windows
Save time with easy data access and management
Get a broad range of statistics for better analysis
Report results in an easy-to-understand format
“The feature set of 15.0 is irresistible. SPSS 15.0
offers a great set of new features that help convert
an unwieldy dataset into usable information. It
also improves my ability to communicate my
analyses and results to consumers who are
informed, but non-technical.”
– Victor Kogler, Consultant, Contra Costa Health Services
See page 40
Analyze data using comprehensive statistical software
SPSS for Windows, the premier statistical
software product for data analysis and data
management, helps solve your business
and research problems. SPSS for Windows
is a modular, tightly integrated, full-featured
product line that allows you to add modules
and products to ensure you meet all your
analytical needs.
SPSS for Windows, unlike other data
analysis packages, is easier to use, has a
correspondingly lower total cost of ownership,
and comprehensively addresses the entire
analytical process. Underlying this offering
are more than 35 years of SPSS analytical
expertise, assuring users that the included
statistics and procedures are tried, tested,
and proven as among the best in the field.
Prepare your data in a flash
Clustering technique handles large datasets
Now you are ready to prepare your data for
analysis—with SPSS 15.0 you can do this
more quickly and easily. Eliminate the timeconsuming task of labeling all your data.
Create your labels once and the Define
Variable Properties tool copies and presents
your labels to your entire dataset. The Identify
Duplicate Cases tool and the Restructure
Data Wizard help make sure your data is
clear and organized properly for analysis.
Get the most accurate identification of clusters
in your data with the TwoStep Cluster procedure
in SPSS 15.0. This state-of-the-art algorithm
will allow you to find clusters in large and
mixed datasets with continuous and categorical
level variables.
Prepare continuous-level data for analysis
The Visual Bander allows you to easily create
bands (such as breaking income into bands
of $10,000 or ages into demographic groups).
A data pass creates a histogram that enables
you to interactively create cutpoints and
automatically create data value labels for them.
Save time with easy data access
Begin your analysis by quickly accessing
massive amounts of data from numerous
database sources with SPSS 15.0’s Database
Wizard—without having to write code or syntax.
The Database Wizard guides you through the
process of accessing data and generates code
in the background.
Plus, with the right drivers, you can connect to
any ODBC-compliant database—resulting in
minimal data handling using conversion-free/
copy-free data access. You’ll save time because
you won’t have to convert data into SPSS
format. SPSS 15.0 also empowers you by giving
you easy access to SAS®, Excel®, and text data.
1.800.253.2575 (U.S. only) Order now!
Powerful statistics for better analysis
Start generating decision-making information
quickly using powerful statistics. SPSS 15.0
empowers you with a broad range of statistics
so you can get the most accurate response for
specific data types. SPSS 15.0’s statistical
lineup includes an extensive variety of
procedures for descriptive analysis, numerical
prediction, group identification, and forecasting.
Present your best results with report OLAP
SPSS 15.0’s report OLAP (Online Analytical
Processing) features give you a fast, flexible
way to create, distribute, and manipulate
information for ad-hoc decision making. Create
frequency tables, graphs, and report cubes that
feature SPSS’ unique, award-winning pivoting
technology. And create comparisons between
past and present data—down to the percent—
with the percent change in OLAP cube feature.
Export your results to a variety of programs
SPSS 15.0 makes it easy to integrate your SPSS
output into your reports by enabling you to
automatically export results into Microsoft® Word,
Excel, PowerPoint®, and as a PDF document. Plus,
you can export your data into Excel and SAS.
Get increased flexibility when you add
SPSS 15.0 modules to your SPSS Base system.
Expand your analytical capabilities to fit any
stage in the analytical process.
For more details and complete specifications, go to
Stages for seamless statistical analysis
Save time with easy data access
What’s New in
SPSS 15.0 for Windows
Get to the analysis stage faster
Enhance your reporting capabilities
Create your favorite charts instantly with an expanded
Chart Builder
Use dual-Y axis and overlay charts for more complex
Easily export SPSS reports as PDF documents
More powerful data management
Quickly access massive amounts of data from numerous
database sources with SPSS’ Database Wizard. SPSS gives
you direct access to Excel, SAS, and text data. You will never
have to waste time re-keying data for analysis.
Continuous-level data made
easy with the Visual Bander
For continuous-level
data, the Visual
Bander lets you
easily create bands
(e.g., breaking ages or
income into specific
ranges). A data pass
provides you with a
histogram that helps
you specify logical
Eliminate the time-consuming task of labeling all your data
with the Define Variables Properties tool. Create your labels
once, and the Define Variable Properties tool copies and
presents your labels to your entire dataset.
Present your best results
with report OLAP
Create, distribute, and manipulate information for adhoc decision making—featuring SPSS’ award-wining
pivoting technology.
Get a broad range of
statistics for your analysis
Get a wealth of statistical
techniques—from data
displays to prediction—so
you can choose the most
accurate procedure to
solve your business and
research problems.
Expanded programmability functionality
Use plug-ins for Python® or the .NET version of
Microsoft Visual Basic® to create applications
Gain access to the SPSS backend through an open
extension to write code using Python, and include
it within SPSS production syntax jobs
Add user-defined procedures to SPSS, provide a
user interface, and send results from these procedures
into an SPSS pivot table in the Output Viewer
SPSS Advanced Models —
two new popular statistics
SPSS Complex Samples —
ordinal regression and SRS estimators
SPSS Data Preparation —
new optimal binning options
Select from three types of optimal binning for
preprocessing data prior to model building:
Unsupervised, supervised, and hybrid approach
Report your results in a
format everyone can access
Export your SPSS results directly into
Microsoft Word and Microsoft Excel.
Plus, you can export your SPSS data into
Excel or SAS, or as a PDF document.
For more details and complete specifications, go to
Easily write back to databases form SPSS by using
the Export to Database Wizard
Create your own dictionary information for variables
with Custom Attributes
Work more easily with very wide data files by using
customized Variable Sets
1.800.253.2575 (U.S. only) Order now!
Continued from page 7
New features and
SPSS 15.0 for Windows
Enhanced reporting capabilities
Use the Chart Builder to create
a variety of commonly used
charts, such as box plots
(shown here), histograms,
and scatterplot matrices, with
ease. This highly visual chart
creation interface enables you
to create a chart by dragging
variables and elements onto a
chart creation canvas.
Expand your chart-making capabilities with
Chart Builder. In a just a few clicks, you can create
popular chart types with this highly visual chart creation
interface. This intuitive, powerful interface will help you
to save time and expand your chart creation capabilities.
Enhance your graphs with these new features
available in Chart Builder:
New chart types: Box plot, high-low, histogram, and more
3-D bar charts displaying simultaneous clustering
and stacking
In SPSS 15.0, you can create
an overlay chart with two
independent Y axes based
on different scales. This
chart shows appraised land
value and appraised value of
improvements plotted against
sale price—all in one frame.
Paneling and summaries of separate variables
Line charts displaying error bars and variables on drop lines
Error bars on bar and area charts
New chart types: Dual-Y axis and overlay charts. These
charts will enable more complex analysis, reveal complex
information, and allow for more sophisticated reporting.
Export SPSS reports as
PDF documents, making
it even easier to share
results with others.
Export SPSS reports as PDF documents. SPSS 15.0 makes
it even easier to share results with others.
Product specifications
Symbol indicates a new feature.
Data access and data export
■ Open multiple data files simultaneously in a single SPSS session
■ Stata data file import/export
■ Dimension data model, enabling
you to import/export data to/from
Dimensions products
■ Ability to import from and export
to OLE DB data sources without
having to go through ODBC
■ Database Wizard
■ Import SAS data
■ Text Wizard
■ Import/export Excel data
■ Easily write back to databases
from SPSS by using the Database
Wizard. For example, you can:
– Create a new table and export it
to your database
– Add new rows to an existing table
– Add new columns to an
existing table
– Export data to existing columns
in a table
■ Save comma-separated value (CSV)
text files from SPSS data files
■ Export output to PowerPoint, Word,
and Excel
Data management and preparation
■ Prepare continuous-level data
for analysis with the Visual Bander
■ Create your own custom programs
with the Output Management
System. Turn output from SPSS
procedures into data and create
your own programs for:
Bootstrapping; Jackknifing and
Leaving One Out methods; and
Monte Carlo simulations
– Create custom routines in SPSS
with the OMS Control Panel
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Easily clean your data when you
identify duplicate records with the
Identify Duplicate Cases tool
Make sense and keep track
of your data files by adding
notes to them with the Data File
Comments command
Create read-only datasets
More accurately describe your
data using longer variable names
(up to 64 bytes)
Create value labels up to
120 characters (double that of
previous versions)
Clone or duplicate datasets
Apply an extended Variable
Properties command to customize
properties for individual users
Longer text stings (up to
32,000 bytes)
Define Variables Properties tool
Copy Data Properties tool
Data Restructure Wizard
Aggregate data to external or to the
active data file
Automatically convert string variables to numeric with Autorecode
– Use an autorecode template to
append existing recode schemes
– Recode a set of variables that has
a single scheme at one time
– Autorecode blank strings so they
are defined as “user-missing”
Date and Time Wizard:
– Easily work with data containing
time and dates in SPSS
– Create a time/date variable from
a string containing a date variable
– Create a time/date variable from
variables that include individual
date units, such as month or year
– Calculate times and dates
– Separate a date unit from a time/
date variable
Apply splitters in the Data Editor
for easier viewing of wide or long
data files
Create your own dictionary information for variables by using Custom
Attributes. For example, create a
custom attribute describing transformations for a derived variable
with information explaining how it
was transformed.
■ Customize the viewing of extremely
wide files with Variable Sets. You
can instantly reduce the variables
shown in the Variable View and
Data View windows to a subset
while keeping the entire file loaded
and available for analysis.
■ Easily find and replace text
strings in your data using the
find/replace function
■ 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
■ Compute new variables using
arithmetic, cross-case, date and
time, logical, missing-value,
random-number, statistical, or
string functions
■ Count occurrences of values
across variables
■ Make transformations permanent
or temporary
■ Execute transformations immediately, batched or on demand
Descriptive statistics
■ Crosstabulations
■ Frequencies; Descriptives; Explore;
Descriptive ratio statistics
Bivariate statistics
■ Means; t tests; ANOVA; Correlation
(Bivariate, Partial, Distances); and
For more details and complete specifications, go to
capabilities in SPSS 15.0 for Windows
More powerful data management
Expanded programmability
functionality helps make SPSS for Windows one
Easily write back to databases from SPSS by using
the Export to Database Wizard in the interface—
previously possible only through syntax
“SPSS 15.0 represents a
valuable and important
upgrade to an excellent
tool, particularly for survey
researchers because of
the data management and
manipulation features.”
of the most powerful statistical development platforms.
In SPSS 15.0, you can:
Use plug-ins for Python® or the .NET version of
Microsoft® Visual Basic® to create applications
Create your own dictionary information for variables
with Custom Attributes. For example, create custom
attributes by including information explaining how
you transformed existing variables.
Gain access to the SPSS backend through an
open extension. This enables you to write code
using Python and include it within SPSS
production syntax jobs.
Work more easily with very wide data files by
using customized variable sets. Instantly reduce
the variables viewed in the Variable View and
Data View windows, while keeping the entire
file available for analysis.
– King Douglas, Senior Analyst,
American Airlines
Introduce additional analytic functionality to SPSS.
Add user-defined procedures to SPSS, provide an
interface, and send result from these procedures
into an SPSS pivot table in the Output Viewer.
* Previously in SPSS Advanced Models
Non-parametric tests
Prediction for numerical outcomes
and identifying groups
■ Factor Analysis
■ K-means Cluster Analysis
■ Hierarchical Cluster Analysis
■ TwoStep Cluster Analysis
■ Discriminant
■ Linear Regression
■ Ordinal regression—PLUM*
■ Reports
– OLAP cubes
– Case summaries
– Report summaries
■ Categorical charts
– 3-D Bar: Simple, cluster, and
– Bar: Simple, cluster, stacked,
dropped shadow, and 3-D
– Line: Simple, multiple, and
– Area: Simple and stacked
– Pie: Simple, exploding, and
3-D effect
– High-low: High-low-close,
difference area, and range bar
– Box plot: Simple and clustered
– Error bar: Simple and clustered
– Error bars: Add to bar, line and
area charts; and confidence
level, S.D, or S.E.
– Dual-Y axis and overlay
– Simple, grouped, scatterplot
matrix, and 3-D
– Fit lines: Linear, quadratic,
or cubic regression; Lowess
smoother; confidence interval
control; and for total or subgroups, display spikes to line
– Bin points by color or marker size
to prevent overlap
Density charts
– Population pyramids: Mirrored
axis to compare distributions;
with or without normal curve
For more details and complete specifications, go to
– Dot charts: Stacked dots
show distribution; symmetric,
stacked, and linear
– Histograms: With or
without normal curve; custom
binning options
Quality control charts
– Pareto, X-Bar, range, Sigma,
individual chart, or moving
range chart
– Automatic flagging of points that
violate Shewhart rules, the ability
to turn off rules, and the ability to
suppress charts
Diagnostic and exploratory charts
– Caseplots and time-series plots
– Probability plots
– Autocorrelation and partial
autocorrelation function plots
– Cross-correlation function plots
– Receiver-Operating Characteristics
Multiple use charts
– 2-D line charts (with 2 scale axes)
– Charts for multiple response sets
Custom charts
– Graphics Production Language
(GPL), a custom chart creation
language, enables advanced
users to attain a broader range
of chart and option possibilities
than the interface supports to
create mixed charts and more
Editing options
– Automatically sort and reorder categories by label, value, or statistic
– Data value labels: Drag and drop,
add connecting lines, and match
color to subgroup
– Select and edit specific elements
directly within a chart: Colors,
text, and styles
– Choose from a wide range of line
styles and weights
– Display gridlines, reference
lines, legends, titles, footnotes,
and annotations
– Y=X reference line
Layout options
– Paneled charts: Create a table
of subcharts, one panel per
level or condition; multiple row
and columns
– 3-D effects: Rotate, modify depth,
and display backplanes
Chart templates
– Save selected characteristics of
a chart and apply them to others
– Apply the following attributes
at creation or edit time: Layout,
titles, footnotes, and annotations;
chart element styles; data element
styles; axis scale range; axis scale
settings; fit and reference lines;
and scatterplot point binning
– Tree-view layout and finer control
of template bundles
Export SPSS output to PDF
– Choose to optimize the PDF for
Web viewing
– Control whether PDF-generated
bookmarks correspond to
Navigator Outline entries in
the Output Viewer. Bookmarks
facilitate navigation of large
– Control whether fonts are
embedded in the document.
Embedded fonts ensure that the
reader of your document sees
the text in its original font,
preventing font substitution.
■ Easily open/save and create new
output files through syntax
System requirements
■ Operating system: Microsoft
Windows XP or 2000
■ Hardware: Intel Pentium®compatible processor
■ Memory: 256MB RAM minimum
■ Minimum free drive space: 400MB
■ SVGA monitor
■ Web browser: Internet Explorer 6
1.800.253.2575 (U.S. only) Order now!
SPSS Text Analysis for Surveys 2.0
Gain greater analytical value from your text responses
Save time by automating the creation of categories and the categorization of responses
Save money by eliminating or reducing the need for outside coding services
Easily make your survey text responses
usable in quantitative analysis
“Everything about
the software saved
me time, therefore
saving the college
– Denyse Bening, Research
Technician, Grand Rapids
Community College
SPSS Inc. created SPSS Text Analysis for Surveys
to help you gain full value from text responses
without the drudgery and expense associated with
manual coding. Specifically designed for survey
text, SPSS Text Analysis for Surveys is based on
our automated LexiQuest™ natural language
processing (NLP) software technologies. Using
this software, you can automate the creation
of categories and categorization of responses
to transform unstructured survey data into
quantitative data—without having to read text
responses word-for-word.
When you use SPSS Text Analysis for Surveys,
you are empowered to gain greater insight from
text responses using these capabilities:
■ Dictionary-based text-extraction technology:
This product ships with libraries and resources
to automate concept extraction. You can
easily customize these libraries by adding
topic-specific terms to match your needs.
1.800.253.2575 (U.S. only) Order now!
Proven linguistic technologies: This product
is based on SPSS Inc.’s LexiQuest™ natural
language processing (NLP) technologies that
enable you to quickly create categories and
reliably categorize responses
Visualization capabilities aid category
refinement: Use bar charts, Web graphs, and
Web tables to quickly reveal which categories
contain co-occurring responses. Then decide
whether to combine certain categories or to
create new ones that better account for
shared responses.
Reuse and share categories: Save time
and ensure reliability across the same or
similar studies
Export results to SPSS or Excel: Analyze
and graph results in other software for
use in decision making
What’s New in SPSS Text
Analysis for Surveys 2.0
New features and enhancements enable survey
researchers to categorize text responses
more quickly and reliably to support better
quantitative analysis and business decisions.
For instance, now you can:
Share project files with others for
better collaboration
Export categories for reuse by others
in new projects
Create conditional rules to
categorize responses based on more
complex information
Profile categories using reference
variables and bar charts
Track coding progress by “flagging”
For more details and complete specifications, go to
Transform your open-ended text responses into easy-to-analyze data
Import survey text responses
Import text responses from a variety of sources,
including SPSS for Windows, Microsoft Excel, the
SPSS Dimensions™ product family, and any ODBCcompliant database program.
Extract key concepts
Extract key concepts automatically from responses to an open-ended
question. The software creates a list of terms, types, and patterns.
Simply click the “Extract”
button (lower-left pane)
to automatically extract
concepts from the text
responses. The single
and multi-word concepts
are color-coded to reveal
their part of speech, such
as red if negative. The
Data pane shows the full
text of all responses to
the question.
Refine categories
Visualization capabilities enable you to quickly
see which categories share responses. This can
help you to refine categories manually.
Create categories and categorize
text responses
Automatically create categories and categorize responses
using term derivation, term inclusion, a semantic network, or
frequency. Also, categorize responses manually by dragging
terms, types, and responses within the interface.
Click the “Create
Categories based
on Linguistics”
button at upper left
to automatically
create categories
and categorize
Export results for analysis and graphing
When you are satisfied with
your categories, you can export
results either as dichotomies or as
categories. These can be used to
create tables and graphs, either
separately or in combination with
other survey data.
A Web graph showing which categories share
responses enables the user to decide whether to
combine certain categories or to create new ones
that better account for shared responses.
Results can also
be exported to SPSS
to create graphs
that communicate
survey findings.
Export results to SPSS to create crosstabs or whatever your analysis requires.
Product specifications
Symbol indicates a new feature.
Import data from:
■ SPSS (SAV), Dimensions (MDD),
Excel (XLS), and ODBC-compliant
Extract key concepts
■ Extract terms, types, and
patterns automatically using
linguistic resources
■ Supports manual review
and refinement
■ Save extraction results
Create categories
■ Supports the reuse of categories
created in other programs
■ Use linguistic algorithms and a
semantic network to automatically
create categories and categorize
■ Create conditional rules to categorize responses by using extraction
results and Boolean operators
■ Allows the “force-in” of an
unextracted word or phrase
into a category definition and
automatically assigns responses
containing it to that category
■ Allows the “force-in”/”force-out”
of responses into/out of categories
without changing the category
Refine results
■ View response co-occurrence by
using a category bar chart, Web
graph, or Web table
■ Use “flags” to mark responses as
completed or to follow-up
■ Profile categories by overlaying
reference variables onto bar charts
■ Print category lists and
some visualizations
Export results (in the following
file formats):
■ SPSS (SAV) and Excel (XLS)
For more details and complete specifications, go to
Share resources
■ Share project files that contain
extracted results, categories, and
linguistic resources
■ Export categories and definitions
for reuse by others in new projects
Dictionaries (customizable)
■ Type Dictionary: Supports the
grouping of similar terms
■ Substitution Dictionary: Groups
similar terms under a target name
■ Exclude Dictionary: Contains terms
to be ignored during extraction
■ Survey Library: Contains resources
related to pattern rules and
types, as well as a predefined
list of synonyms and excluded
terms (proprietary)
Project Library: Stores dictionary
changes for a particular project
Core Library: Contains reserved
Type Dictionaries for Person,
Location, Product, and Organization
Budget Library: Contains a built-in
type for words or phrases that
represent qualifiers and adjectives
Opinions Library: Contains seven
built-in types that group terms for
qualifiers and adjectives
System requirements
■ Operating system: Microsoft
Windows XP Professional, Service
Pack 1 or higher; Windows 2000
Professional, Service Pack 4
■ Hardware:
– Processor: Intel® Pentium®-class;
3.0 GHz recommended
– Monitor: 1024 x 768 (SVGA) res.
– Memory: 256MB RAM minimum;
512MB recommended; 1GB or
more for large datasets
– Minimum free space: 300MB;
more recommended for larger
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SPSS 13.0 for Mac OS X also features
two new add-on modules and updates
to your favorite modules:
SPSS 13.0 for Mac OS X
Analyze data with the most powerful statistical software for the Mac
Access more reporting capabilities
Save more time with easier data management
Comprehensive statistical
software for your Mac
Symbol indicates a new feature.
What’s New in SPSS 13.0 for
Mac OS X
SPSS 13.0 for Mac OS X provides
a number of new features and
capabilities to help you access
and manage data, create graphics,
and produce output. In addition,
two new add-on modules—SPSS
Classification Trees™ and SPSS
Complex Samples™—enable you to
more accurately work with certain
data types. Enhancements to several
other modules provide you with new
statistics and improve the way you
create and present tables.
SPSS 13.0 for Mac OS X
Data and access management
More powerful data management in
SPSS 13.0 for Mac OS X provides
you with a number of new features
to save time, increase accuracy
and reliability—and better manage
your data.
Graphics and output
Enhanced reporting capabilities in
SPSS 13.0 for Mac OS X enable you
to produce professional-looking
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graphs with ease, while the ability
to export output to commonly
used Microsoft Office applications
gives you more options for communicating results.
What’s New in SPSS 13.0 for
Mac OS X: Add-on Modules
SPSS 13.0 for Mac OS X is further
enhanced by the addition of two
new add-on modules and updates
to several existing add-on modules.
These new and enhanced modules
are described to the right.
SPSS Classification Trees 13.0 — Enables you to better
identify groups, discover relationships between groups, and
predict future events
SPSS Complex Samples 13.0 — Provides the specialized planning
tools and statistics you need when working with sample survey data
Mac users can truly get excited about SPSS 13.0 for Mac OS X. It
represents a significant upgrade from earlier versions—delivering
major advances in access and data management, graphics creation,
and output production.
Product specifications
NEW for
Mac OS’s
to get excited!
New add-on module:
SPSS Classification Trees
SPSS Classification Trees enables
you to better identify groups,
discover relationships between
groups, and predict future events.
Create highly visual classification
and decision trees that allow you
to present categorical results in an
intuitive manner—so you can
more clearly explain categorical
results to non-technical audiences.
You can easily explore your results
and visually determine how models
flow. This can help you find specific
subgroups and relationships
that you might not uncover using
more traditional statistics.
SPSS Tables 13.0 — Preview tables as you build them with the
new interactive table preview builder
SPSS Regression Models 13.0 — Predict categorical
outcomes with more than two categories with multinomial
logistic regression (MLR)
SPSS Categories 13.0 — Reveal underlying relationships
between two or more nominal variables
SPSS Advanced Models 13.0 — Expand on the GLM technique
with the linear mixed models procedure and achieve more
accurate models
SPSS Classification Trees includes
four established tree-growing
■ Exhaustive CHAID
■ Classification and regression trees
New add-on module:
SPSS Complex Samples
Do you analyze data from survey or
market research, public health datasets, or government agencies? Do you
use sample survey methodology in
your research, or are your data likely
to come from a public-use data
set that includes complex sample
designs? If you do, you need SPSS
Complex Samples.
Complex sample designs require
specialized statistical techniques
to account for the sample design
and its associated standard errors.
SPSS Complex Samples provides
the specialized planning tools and
statistics you need when working
with sample survey data. It enables
you to make more statistically valid
inferences for a population by
incorporating the sample design into
survey analysis. Two algorithms—
complex samples general linear
For more details and complete specifications, go to
Discover what’s new in SPSS 13.0 for Mac OS X
SPSS 13.0 for Mac OS X features more powerful
data management—giving you many new
features to save time, and increase accuracy
and reliability:
■ The new Visual Bander enables you to
easily create bands (for example, break
income into “bands” of 10,000 or break
ages into groups).
■ The new Date and Time Wizard lets you
calculate dates and times, such as
calculating the length of time someone
has been a patient.
■ Clean your data by identifying duplicate
records through the user interface with the
Identify Duplicate Cases tool.
■ More accurately describe your data by using
longer variable names—expanded from eight
bytes to 64 bytes.
■ Create your own custom programs with the
output management system (OMS).
model (GLM) and complex samples
logistic regression—enable you to
more accurately work with numerical
and categorical outcomes in complex
sample designs.
Enhanced add-on module:
SPSS Tables
With SPSS Tables’ new interactive
table preview builder, you can simplify the creation of custom tables by
previewing tables as you build them.
Simply drag your variables onto the
table preview builder and make
adjustments as necessary. Gain a
high level of control over the layout
and appearance of your tables by
using category management features.
Using the new
Visual Bander,
you can specify
cutpoints in your
data. Here, the user
created interval
cutpoints based
on amount spent.
Produce professional-looking graphs with ease
using the enhanced reporting capabilities in
SPSS 13.0 for Mac OS X. The new presentation
graphics system makes it easy to create the graph
you want and produce readable graph output:
■ Export output to commonly used Microsoft
Office applications, expanding your options
for communicating results. Export SPSS
output to Excel, Word, and PowerPoint.
■ Choose from new chart types such as
population pyramids, 3-D bar charts, error
bar charts, and dot charts.
■ Further improve chart appearance by using
additional chart display features and
options, such as paneled charts, error bars
on categorical charts, and data value labels
that you can position anyplace on your chart.
■ Switch output language (e.g., switch between
Japanese and English).
For example, make your tables more
precise by changing variable types
or excluding categories. Additionally,
use inferential statistics (or significance testing) in your tables to perform common analyses. SPSS Tables
includes the following statistics:
■ Chi-square test of independence
■ Comparison of column means
(t test)
■ Comparison of column proportions
(z test)
All of these enhancements make
it easier for you to summarize and
communicate even the most complicated results in a presentation-ready
table format.
For more details and complete specifications, go to
Enhanced add-on module:
SPSS Regression Models
Easily find the best predictor from
dozens of possible predictors by
using the new stepwise function in
multinomial logistic regression.
Enhanced add-on module:
SPSS Categories
The new multiple correspondence
analysis procedure in SPSS
Categories greatly enhances this
add-on module’s functionality. Use
it to reveal the underlying relationships between two or more nominal
variables when similar categories
are grouped close to one another in
a chart.
New chart types make data
reporting easier to read
and understand. Here, the
chart dynamically displays
customers’ shopping styles.
Enhanced add-on module:
SPSS Advanced Models
The linear mixed models procedure
enables you to model means, variances, and covariances in your data.
Several new techniques to help you
create more accurate predictive
models are now included in this procedure. For example, you can:
■ Select from 11 non-spatial covariance types, including first-order
ante-dependence, heterogeneous,
and first-order autoregressive
■ Save the standard error of prediction obtained from the procedure
■ Display the dependent variable’s
estimated marginal means in
cells and their standard errors
for the specified factors by using
the means subcommand for
fixed effects
Minimum system requirements
Operating system: Mac OS X
version 10.3.9 running Java™ 1.4.2
■ Processor: G4 667MHz
■ Memory: 256MB RAM (512MB
recommended for installations
using Mac OS X Tiger™)
■ Monitor: Color monitor, 1024 x 768
Please note: SPSS does not support
the use of any existing version of
SPSS for Mac OS X on the new Intel®based Mac hardware. Some of the
procedures result in incorrect numerical calculations under the Rosetta
emulation software, rendering the
results invalid. SPSS 13.0 for Mac OS
X will prevent installation onto Intelbased hardware.
The Intel-compatible version of SPSS
for Mac OS X—SPSS 15.0 for Mac
OS X—will be released in the second
quarter of 2007.
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SPSS products help you
through each step
Analytical projects can easily become a daunting
task. So many steps and so little time—from
planning and collection, all the way to getting
the reports of your final analysis into the hands
of the right people.
SPSS’ integrated software—spanning the entire
analytical process—will streamline your work.
Since SPSS products work together, you won’t
have to duplicate effort that might be required
when using products from a variety of vendors.
The analytical process is actually an iterative
cycle. Once you’ve deployed your results, you
use feedback about them to begin planning
the next cycle. The diagram below outlines the
steps and identifies SPSS products involved in
each step of the analytical process.
1 2 3
Data collection
Data access
Take the time to plan analytical
projects at the beginning to reduce
costs and wasted resources. Map a
course of action instead of diving into
analysis. SPSS has specially designed
tools to help you estimate your sample
size, launch more successful products,
and more.
Collect clean, unbiased, and up-to-date
information in an efficient manner.
SPSS gives you the tools to do this
effectively, no matter how you’re
collecting data.
Quickly access massive amounts
of data from numerous database
sources by using the Database
Wizard in SPSS. No longer do
you need to write code or syntax—
the Wizard guides you through
the process of accessing data and
generates code in the background.
And, with the right drivers, you
can connect to any ODBCcompliant database—resulting
in minimal data handling using
conversion-free/copy-free data
access. You save time because
you won’t have to convert data
into the SPSS format.
SPSS Complex Samples
SPSS Conjoint
SPSS Data Entry
SPSS for Windows
Data analysis
Data collection
Data access
Data management
and data preparation
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4 5 6 7
Data management
and data
Prepare your data for analysis quickly
with efficient data management and
SPSS for Windows
SPSS Missing Value Analysis
SPSS Data Preparation
Data analysis
The SPSS family of products offers a
full range of statistics and techniques,
including simple statistics, correlations
and crosstabs, clustering techniques,
and factor analysis, as well as predictive analysis, such as regression. SPSS
gives you full support to perform the
right statistical procedures for your data,
based on what you want to learn and
the level of measurement of the variables
to help you maximize your analysis.
Once your analysis is complete, you
need to summarize results so nontechnical audiences can understand
them. SPSS lets you easily condense
and transform massive amounts of
case data into readable, meaningful
information, so you—and others—can
better understand your customers,
patients, or employees.
Put your results in the hands of people
who can use them to make a difference. SPSS makes it easy to securely
share results with others—on the Web,
in presentations, or in publications.
SPSS for Windows
SPSS Regression Models
SPSS Advanced Models
SPSS Exact Tests
SPSS Tables
SPSS Complex Samples
SPSS Categories
SPSS Trends
SPSS Classification Trees
SPSS Text Analysis for Surveys
SmartViewer Web Server
SPSS for Windows
SPSS Classification Trees
SPSS Tables
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SPSS Advanced Models 15.0
Require multiple outcomes
Want to measure outcomes over time
Analyze data with hierarchical structure
Estimate length of time until an event
Move beyond basic analysis
Build flexible models using a wealth of model-building options
Achieve more accurate predictive models using a wide range
of modeling techniques
Enter the realm of powerful and
sophisticated analyses
Created to provide you with more statistical power, SPSS Advanced
Models enables you to reach more accurate conclusions. Consider
what it would be like to harness sophisticated univariate and
multivariate analytical techniques and unleash them on your data.
Break through the barrier between general analysis and advanced
modeling, and begin reaping the rewards today.
It’s time to step up your
analysis when you…
What’s new in SPSS Advanced Models 15.0
When you upgrade to SPSS Advanced
Models 15.0, you get two new popular
statistical procedures...generalized
linear models (GZLMs) and generalized
estimating equations (GEEs). Together
they address a wide range of statistical
modeling problems.
GZLMs and GEEs represent a unifying
framework that includes classical
linear models with normally distributed
dependent variable, logistic, and probit
models for binary data, and loglinear
models for count data, as well as various
other nonstandard regression-type models.
■ GZLMs cover not only widely used
statistical models, such as linear
regression for normally distributed
responses, logistic models for binary
data, and loglinear models for count
data, but also many useful statistical
models via its very general model
■ GEEs extend generalized linear
models to accommodate correlated
longitudinal data and clustered data
It’s a numbers game for Las Vegas casinos
as Vegas, the nation’s gaming mecca, is a
constant draw for people who want to try
their hand at lady luck. In fact, according to
the 2005 AGA Survey of Casinos Entertainment,
a record 54.1 million people visited casinos in
2004 nationwide. In the casino industry, it’s
crucial for gaming organizations to carve a
niche in the market.
A large gaming organization needed answers
to some very significant questions to prepare
a market strategy.
■ What should the casino look like and what
amenities should be featured?
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What factors (both gambling and adjunct
activities) affect visitation?
What type (profile) of people will the casino
attract, to include travel distance?
What is the “predictive” estimate of the
planned casino users?
To assist it in getting the answers to these
questions, the organization hired principal
statistician William Bailey from WMB &
Associates. “This was a huge, complex
project,” stated Bailey. So Bailey turned to
SPSS for Windows and SPSS Advanced
Models to help him to analyze the large
amounts of complex data.
“We created key drivers and factors to
determine the viability of the project, and
suggestions regarding activities, beyond
gaming tables, that would encourage visiting
the casino,” said Bailey. This offered
guidance for his client to design the optimal
floor plan and entertainment for the casino.
Hundreds of pages of numbers and tables were
generated from the data. Bailey and his team
created 20-30 perceptual maps with SPSS.
From this data they were able to make their
findings more understandable for the client.
“The client very favorably received our
presentation, partly because SPSS allowed
us to give them answers in an easy-tounderstand format. Even though this was
such an enormous job, I knew SPSS for
Windows with SPSS Advanced Models
could easily handle the project,” said Bailey.
“I have a tremendous amount of
confidence in SPSS Advanced Models.”
– William Bailey, Principal Statistician
at WMB & Associates
For more details and complete specifications, go to
Comprehensive tools for today’s analyst
SPSS Advanced Models meets the wide-range of
statistical needs of today’s analyst. With its wealth
of features and capabilities, you will never be limited
to basic analytical techniques again. Along with
what’s new, check out the powerful procedures you
can choose from.
General linear models (GLM) multivariate: Gain more
flexibility to describe the relationship between a
dependent variable and a set of independent variables.
GLM doesn’t leave you limited to a single data type,
but offers a wealth of model-building possibilities.
Product specifications
Achieve more accurate
A marketing group tests
three campaigns
to determine which promotion has the greatest
effect on sales.
Identify random effects
The marketing group
specifies the fixed (promotion) and random effects
(market ID) in a model to
adjust for the covariance
structure of the data.
Linear mixed models (Mixed): Expand on the GLM
technique with the Mixed procedure and achieve
more accurate models. Use the Mixed procedure to
model means, variances, and covariances in your
data when working with nested-structure data. Or
use Mixed when working with repeated measures
data, including situations in which there are different
numbers of repeated measurements or different
intervals for different cases, or both.
Survival analysis: Analyze event history and duration
data to better understand events. SPSS Advanced
Models includes state-of-the-art survival procedures
such as Kaplan-Meier and Cox Regression.
Variance component estimation (VARCOMP): Choose
from a number of methods to estimate the variance
component for each random effect in a mixed model.
Follow up your GLM analysis with variance component
estimation analysis to estimate the variance. It’s easy
to determine where to focus your attention when
reducing the variance.
Symbol indicates a new feature.
GZLMs and GEEs
GZLMs procedures provide a
unifying framework that includes
classical linear models with normally distributed dependent variable, logistic, and probit models
for binary data, and loglinear
models for count data, as well
as various other nonstandard
regression-type models. GEEs
procedures extend the generalized linear model to correlated
longitudinal data and clustered
data. Particularly, GEEs model
correlations within subjects.
■ Provides a common framework
for the following outcomes:
continuous outcomes, count
data, event/trial data, claim
data, and correlated responses
within subjects
■ Specify model effects,
an offset or scale weight
variable if either exists,
the probability distribution,
and the link function
– Include or exclude the
– Specify an offset variable or
fix the offset at a number
– Specify a variable that contains Omega weight values
for the scale parameter
– Choose from probability
distributions: Binomial,
Gamma, inverse Gaussian,
negative binomial, normal,
and Poisson
– Choose link functions:
Complementary loglog, identity, log, log
complement, logit, negative
binomial, negative
log-log, odds power, probit,
and power
Control statistical criteria for
generalized linear models and
specify numerical tolerance for
checking singularity. Specify:
– Type of analysis for each
model effect: Type I, Type III,
or both
– A value for starting iteration
for checking complete and
quasi-complete separation
– The confidence interval level
for coefficient estimates and
estimated marginal means
– Parameter estimate covariance matrix: Model-based
estimator or robust estimator
– Hessian convergence
– Initial values for parameter
– Log-likelihood convergence
– Form of the log-likelihood
– Maximum number of
iterations for parameter
estimation and log-likelihood
– Maximum number of steps in
step-halving method
– Model parameters estimation
method: Fisher scoring
method or Newton-Raphson
Use the new GZLMs procedure
Fit a wide variety of
models for many
types of outcomes
with generalized
linear models. For
example, fit a Gamma
regression for skewed
outcomes, such as
claim amounts.
Loglinear analysis: Fit loglinear and logit models
to count data so you can easily model and predict
your outcomes.
Analyze results using various methods
The random effects variance is significant and larger than the
residual variance. Most of the variability unaccounted for by the
fixed effects is due to the market-to-market sales variation.
For more details and complete specifications, go to
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Continued from page 17
Product specifications
– Parameter convergence criterion
– Method of fitting the scale
parameter: Maximum likelihood,
deviance, Pearson Chi-square, or
fixed at a number
– Tolerance value used to test
for singularity
Specify the working correlation
matrix structure used by the GEE to
model correlations within subjects,
and control statistical criteria in
the non-likelihood-based iterative
fitting algorithm. Specify:
– The within-subject or time effect
– Correlation matrix structure:
Independent working, AR(1)
working, exchangeable working,
fixed working, m-dependent
working, and unstructured working
– Whether to adjust the working
correlation matrix estimator by
the number of non-redundant
– Whether to use robust or the
model-based estimator or
parameter estimate covariance
matrix for GEEs
– The Hessian convergence
criterion for the GEEs
– Maximum iterations
– Relative or absolute parameter
convergence criterion
– The number of iterations
between updates of the working
correlation matrix
Display estimated marginal means
of the dependent variable for all
level combinations of a set of
factors. Specify:
– The cells for which estimated
marginal means are displayed
– The covariate values to use
when computing the estimated
marginal means
– Whether to compute estimated
marginal means based on the
original scale of the dependent
variable or on the link function
– The factor or set of crossed
factors, the levels or level combinations which are compared
using the contrast type specified
– The type of contrast to use for
the levels of the factor, or level
combinations of the crossed factors. The following contrast types
are available: Pairwise, deviation,
difference, Helmert, polynomial,
repeated, and simple.
– The method of adjusting the
significance level used in tests
of the contrasts: Least significant
difference, Bonferroni, Sequential
Bonferroni, Sidak, and Sequential
■ Display the following: Correlation
matrix for parameter estimates,
covariance matrix for parameter
estimates, case processing summary, descriptive statistics, goodness
of fit, general estimable function,
iteration history, Lagrange multiplier test, set of contrast coefficient
(L) matrices, model information,
parameter estimates and
corresponding statistics, model
summary statistics, and working
correlation matrix
■ Save to the working data file:
Predicted value of the linear predictor, estimated standard error of the
predicted value of the linear predictor, predicted value of the mean of
the response, confidence interval
for the mean of the response, leverage value, raw residual, Pearson
residual, deviance residual, standardized Pearson residual, standardized deviance residual, likelihood residual, and Cook’s distance
■ Save to an external file: The parameter correlation matrix and other
statistics to an SPSS dataset, the
parameter covariance matrix and
other statistics to an SPSS dataset,
and the parameter estimates and
the parameter covariance matrix
to an XML file
Expands the general linear model
used in the GLM procedure so that
data can exhibit correlation and
non-constant variability
■ Fit the following types of models:
– Fixed effects ANOVA model, randomized complete blocks design,
split-plot design, purely random
effects model, random coefficient model, multilevel analysis,
unconditional linear growth
model, linear growth model with
person-level covariate, repeated
measures analysis, and repeated
measures analysis with timedependent covariate
■ Opt to apply frequency weights or
regression weights
■ Use one of six covariance structures
■ Select from 11 non-spatial covariance types
■ Choose CRITERIA to control the iterative algorithm used in estimation
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and to specify numerical tolerance
for checking singularity
■ Specify the mixed model fixed
effects: No intercept, Type I sum of
squares, and Type III sum of squares
■ Specify the random effects: Identify
the subjects and covariance structure
(first-order autoregressive, compound symmetry, Huynh-Feldt,
identity, and unstructured variance
■ Depending on the covariance type
specified, random effects specified
may be correlated
■ Estimation methods: Maximum
likelihood and restricted maximum
■ Print covariance matrix of residual
■ Specify the residual covariance
matrix in the mixed effects model
■ Save fixed predicted values, predicted values, and residuals
■ Customize hypotheses tests by
specifying null hypotheses as linear combinations of parameters
■ Save standard error of prediction
■ Means subcommand for fixed
effects, which displays the dependent variable’s estimated marginal
means in the cells and its standard
errors for the specified factors
Describe the relationship between
a dependent variable and a set of
independent variables
■ Select univariate and multivariate
lack-of-fit tests
■ Regression model
■ Fixed effect ANOVA, ANCOVA,
■ Random or mixed ANOVA and
■ Repeated measures: Univariate or
■ Doubly multivariate design
■ Four types of sums of squares
■ Full-parameterization approach to
estimate parameters in the model
■ General linear hypothesis testing
for parameters in the model
■ Write a covariance or correlation
matrix of the parameter estimates
in the model in a matrix data file
■ Plots: Spread vs. level, residual,
and profile
■ Post hoc tests for observed
cell means
■ Estimated population marginal
means for predicted cell means
■ Save variables to the active file:
Unstandardized predicted values,
weighted unstandardized predicted
values, unstandardized residuals,
weighted unstandardized residuals, deleted residuals, standardized
residuals, Studentized residuals,
standard errors of predicted value,
Cook’s distance, and uncentered
leverage values
■ Pairwise comparisons of expected
marginal means
■ Linear hypothesis testing of an
effect vs. a linear combination
of effects
■ Option to save design matrices
■ Contrasts: Deviations, simple,
difference, Helmert, polynomial,
repeated, and special
■ Print: Descriptive statistics, tests of
homogeneity of variance, parameter
estimates, partial Eta2, general
estimable function table, lack-of-fit
tests, observed power for
each test, and a set of contrast
coefficient (L) matrices
Variance component estimation
■ Estimation methods: ANOVA
MINQUE, maximum likelihood, and
restricted maximum likelihood
■ Type I and Type III sums of squares
for the ANOVA method
■ Choices of zero-weight or uniformweight methods
■ Choices of ML and REML
calculation methods
■ Save variance components estimates and covariance matrices
■ Print: Expected mean squares, iteration history, and sums of squares
LOGLINEAR (For a full description, see
Hierarchical loglinear models for
multiway contingency tables
■ Simultaneous entry and backward
elimination methods
■ Print: Frequencies and residuals
■ Parameter estimates and partial
associations for saturated models
■ Criteria specification: Convergence,
maximum iterations, probability
of Chi-square for model, and
maximum steps
■ Specified cell weights and maximum
order of terms
■ Plots of standardized residuals vs.
observed and expected counts
■ Normal probability plots of
standardized residuals
Fit loglinear and logit models to
count data by means of a generalized
linear model approach
Model fit, using ML estimation
under Poisson loglinear model and
multinomial loglinear models
■ Accommodate structural zeros
■ Generalized log-odds ratio facility
tests the specific generalized logodds ratios are equal to zero, and
can print confidence intervals
■ Diagnostic plots: Scatterplots and
normal probability plots of residuals
■ Criteria specification: Confidence
interval, iterations, convergence,
Delta, and Epsilon values used as
tolerance in checking singularity
Analysis of life tables
■ Life tables for individual groups
■ Interval variable lengths
■ Plots: Cumulative survival distribution on log or linear scale, hazard
function, and density function
■ Comparisons of subgroups
■ Plots of the one-minus
survival function
■ Status variables to indicate if
the terminal event occurred for
the observation
■ Print life tables
■ Calculate comparisons of the subgroups: Exact, approximate, conditional, pairwise, and compare
■ Option to write survival table data
records and label records files
Estimates the length of time to an
event using Kaplan-Meier estimation
■ Define factors and strata
■ Plots: Cumulative hazard functions,
cumulative, and log survival
■ Display censored cases
■ Save variables to a file: Cumulative
number of events, hazard, standard
error, and survival function
■ Statistical display: Cumulative
events and survival, mean and
median survival times with standard
errors, number at risk, requested
percentiles, and standard error
■ Tests for equality of survival
distributions: Breslow, Tarone,
and logrank
■ Specify a trend component for
factor levels having a metric
■ Include plots of the one-minus
survival function
■ Status variables to indicate if
the terminal event occurred for
the observation
■ Specify strata within categories
of factors
■ Compare the survival distributions
for different levels of the factor:
Compare all factor levels in a single
test, compare each pair of factors,
pool the test statistic across
all strata, and compare the factor
levels for each stratum
Proportional hazards with timedependent covariates
■ Contrasts: Deviations, simple,
difference, Helmert, polynomial,
repeated, special, and indicator
■ Define strata to estimate separate
baseline functions
■ Methods: Backward and forward
stepwise and direct entry
■ Plots: Cumulative survival,
hazard, and log-minus-log plots
for each stratum
■ Removal of variables: Change
in likelihood ratio, conditional,
and Wald
■ Save variables to files: Baseline
survival and hazard functions and
their standard errors, cumulative
hazard function, dfbeta, log-minuslog of survival function, residuals,
and survival function
■ Plots of the one-minus
survival function
■ Status variables to indicate if
the terminal event occurred for
the observation
■ Ordinal or nominal predictors
■ Print: Full regression output including overall model statistics for
variables in the equation and variables not in the equation, summary
information, correlation/covariance
matrix of the parameter estimates
for the variables in the model,
baseline table, and confidence
intervals for exponential of Beta
■ Criteria: Change in parameter
estimates for terminating iteration;
maximum number of iterations;
percentage of change in log-likelihood ratio for terminating iteration;
probability of score statistic for variable entry; and probability of Wald,
likelihood ratio (LR), or conditional
LR statistic to remove a variable
■ Specify the pattern of covariate values to be used for requested plots
and coefficient tables
■ Write to external SPSS data files:
Coefficients in the final model and
survival table
System requirements
■ SPSS 15.0 for Windows
■ Other system requirements vary
according to platform
For more details and complete specifications, go to
Join us at the next See it in SPSS seminar
and see the new ways SPSS can support
your data analysis efforts.
See it in SPSS
Agenda & Registration
See it in SPSS is a
free, half-day event
for SPSS customers,
featuring seminars
highlighting the
newest SPSS products,
a complimentary
networking lunch, and
optional afternoon
8:30 – 9:00 a.m.
Introduction & Welcome
9:00 – 9:15 a.m.
mrInterview 3.5
9:15 – 10:00 a.m.
SPSS Text Analysis For Surveys 2.0
10:00 – 10:45 a.m.
10:45 – 11:00 a.m.
Enhance SPSS with modules
11:00 – 11:30 a.m.
SPSS 15.0 for Windows
11:30 a.m. – 12:15 p.m.
12:15 – 12:30 p.m.
Complimentary networking lunch:
12:30 – 1:30 p.m.
Optional afternoon sessions:
Data Mining with Clementine 10.0
1:30 – 2:30 p.m.
2:30 – 3:30 p.m. (repeat)
Discover how the newest version of Clementine offers
you unbeatable productivity for data mining applications.
Features include:
■ Better access to Dimensions survey data
■ New “anomaly detection” algorithm
■ New “feature selection” for CRM marketing applications
Go to:
for the schedule of upcoming cities.
Announced cities include:
New York, Chicago, Sacramento, Los Angeles, and Toronto
End-to-end Survey Research Solutions
1:30 – 2:30 p.m.
2:30 – 3:30 p.m. (repeat)
See how Dimensions helps you gain more value from
customer interactions and improve your strategic planning
and responsiveness to change.
Rely on SPSS Categories
whenever you need to:
SPSS Categories 15.0
Visualize how rows and columns of large tables
of counts or means relate
Visualize and explore multivariate categorical data
Understand information in large two-way and multi-way tables
See relationships in your data using revealing biplots
and perceptual maps
Determine how closely customers perceive
your products to others in your offering set
or your competitors’
Understand what characteristics consumers relate
to most regarding product or brand
Reveal underlying relationships through
perceptual maps of categorical data
Work with and understand ordinal and nominal
data with procedures similar to conventional regression,
principal components, and canonical correlation
Perform regression analysis with a categorical
dependent variable
With SPSS Categories’ sophisticated procedures in your toolbox, you are no longer
hampered by categorical or highly-dimensional data. These techniques ensure you
have all the tools you need to easily analyze and interpret your multivariate data
and its relationships more completely.
Present your results clearly using perceptual maps
Use dimension reduction techniques to go beyond unwieldy tables to clearly
see relationships in your data using revealing perceptual maps and biplots.
Summary charts display similar variables or categories, providing you with insight
into relationships among more than two variables.
The data are a 2x5x6 table containing information on two genders,
five age groups, and six products. This plot shows the results of a
two-dimensional multiple correspondence analysis of the table.
Notice that products such as “A” and “B” are chosen at younger ages
and by males, while products such as “G” and “C” are preferred at
older ages.
Product specifications
Multidimensional unfolding analysis
■ Read one or more rectangular
matrices of proximities
■ Read weights, initial configurations,
and fixed coordinates
■ Optionally transform proximities
with linear, ordinal, smooth ordinal,
or spline functions
Specify multidimensional unfolding
with identity, weighted Euclidean,
or generalized Euclidean models
Specify fixed row and column coordinates to restrict the configuration
Specify initial configuration
(classical triangle, classical
Spearman, Ross-Cliff, correspondence, centroids, random starts,
or custom), iteration criteria, and
penalty parameters
Specify plots for multiple starts,
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initial common space, stress per
dimension, final common space,
space weights, individual spaces,
scatterplot of fit, residuals
plot, transformation plots, and
Shepard plots
Specify output that includes the
input data, multiple starts,
initial common space, iteration
history, fit measures, stress
decomposition, final common
space, space weights, individual
spaces, fitted distances, and
transformed proximities
Write common space coordinates,
individual weights, distances, and
transformed proximities to a file
Statistics: Iteration history, stress
measures, stress decomposition,
coordinates of the common space,
object distances within the
final configuration, individual
space weights, individual spaces,
transformed proximities, and
transformed independent variables
Plots: Stress plots, common space
scatterplots, individual space
weight scatterplots, individual
spaces scatterplots, transformation
plots, Shepard residual plots, independent variables transformation
plots, and correlations plots
Statistics: Frequencies, missing
values, optimal scaling level,
mode, variance accounted for by
centroid coordinates, vector
coordinates, total per variable and
per dimension, component
loadings for vector-quantified
variables, category quantifications
and coordinates, iteration history,
correlations of the transformed
variables and eigenvalues of the
For more details and complete specifications, go to
Unleash the full potential of your data through optimal scaling
and dimension reduction techniques
Correspondence analysis (CORRESPONDENCE): Describe the
relationships between two nominal variables in a low-dimensional
space, while simultaneously describing the relationships between
categories for each variable.
Analyze differences between categories
Incorporate supplementary
Use correspondence analysis to easily
display and analyze differences between
categories. In this example, researchers
present and analyze the two-dimension
table relating staff group to smoking
category in a particular workplace.
Categorical regression (CATREG): Predict the values of a
categorical dependent variable from a combination of categorical
independent variables.
Multiple correspondence analysis (MULTIPLE CORRESPONDENCE):
Analyze a categorical multivariate data matrix when all the variables
are analyzed at the nominal level. Similar to correspondence analysis
except it doesn’t limit you to only two variables.
Categorical Principal Components Analysis (CATPCA): Use alternating
least squares to generalize principal components analysis to
accommodate variables of mixed measurement levels. Specify a
transformation type of nominal, ordinal, or numeric on a variable-byvariable basis.
Nonlinear canonical correlation (OVERALS): Use alternating least
squares to generalize canonical correlation analysis. It allows more than
one set of variables to be compared to one another on the same graph.
Proximity scaling (PROXSCAL): Takes a matrix of similarity and
dissimilarity distances between observations in a high-dimensional
space and assigns them to a position in a low-dimensional space in
order for you to gain “spatial” understanding of how objects relate.
Uncover associations
and relationships
Using symmetrical
normalization to produce
a biplot, the researchers
conclude that a strong
association does not exist
in the sample between
non-drinking and nonsmoking, and there is
a suggested association
between drinking and
level of smoking with
relatively more drinkers in
the high-smoking group.
Easily incorporate supplementary information on additional variables in SPSS Categories. Here, additional
information on alcohol consumption by staff group is
known. These additional columns can be projected into the
staff group by smoking category space.
Preference scaling (PREFSCAL): Set up the Preference Scaling procedure
(PREFSCAL) in syntax to perform multidimensional unfolding on two sets
of objects in order to find a common quantitative scale.
correlation matrix, correlations
of the original variables and eigenvalues of the correlation matrix,
and object scores
Plots: Joint category plots, transformation plots, residual plots,
projected centroid plots, object
plots, biplots, triplots, and
component loadings plots
■ Statistics: Correspondence
measures; row and column profiles;
singular values; row and column
scores; inertia, mass, row, and column score confidence statistics; and
singular value confidence statistics
■ Plots: Transformation plots, row
point plots, column point plots,
and biplots
■ Statistics: Frequencies, regression
coefficients, ANOVA table, iteration
history, and category quantifications
Plots: Correlations between
untransformed predictors,
correlations between transformed
predictors, residual plots, and
transformation plots
■ Statistics: Model summary; history
statistics, descriptive statistics;
discrimination measures; category
quantifications; inertia of the
For more details and complete specifications, go to
categories; contribution of the
categories to the inertia of the
dimensions and contribution of
the dimensions to the inertia of
the categories; iteration history,
correlations of the transformed
variables, and the eigenvalues of
this correlation matrix; correlations of the original variables and
the eigenvalues of this correlation
matrix; and object scores
Plots: Object points, category
points (centroid coordinates),
discrimination measures,
transformation (optimal category
quantifications against category
indicators), residuals per variable,
objects and variables (centroids),
and joint plot of the category points
for the variables in the varlist
■ Statistics: Frequencies, centroids,
iteration history, object scores,
category quantifications, weights,
component loadings, and single
and multiple fit
Plots: Object scores plots, category
coordinates plots, component
loadings plots, category centroids
plots, and transformation plots
System requirements
SPSS 15.0 for Windows
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UNICEF increased direct mail
response by up to 80 percent
SPSS Classification Trees 15.0
Create classification trees directly within SPSS 15.0 for Windows
Identify patterns, segments, and groups in your data
Choose from four established tree-growing algorithms
ith more than 1 million donors, UNICEF Germany
finances its projects to help children in less-developed
countries through fundraising in the form of donations and sales
of greeting cards.
Create classification trees for better
identification of groups directly within
SPSS for Windows
Faced with an increasingly tight budget and a continuously
growing number of competitors, UNICEF Germany turned to
Ogilvy & Mather Dataconsult (O&MDC) to better segment its
target donors. “The goal was to uncover particular donor
characteristics to maximize returns,” said Matthias SingerFischer, Senior Consultant, Ogilvy and Mather Dataconsult.
The SPSS Classification Trees add-on module creates classification and decision trees
directly within SPSS to identify groups, discover relationships, and predict future events.
By creating visual trees, you are able to present results in an intuitive manner—so you
can more clearly explain results to non-technical audiences.
Using five years of historical data from the UNICEF database,
O&MDC gathered approximately 30 variables, including
standard demographics, donation frequency, date of last
donation, sum of all recent donations, and several variables
specific to UNICEF, such as preferred causes.
Why SPSS Classification Trees
should be added to your SPSS desktop:
Access directly within SPSS for
Windows—you never leave the
SPSS environment
Identify groups, segments, and
patterns in a highly visual manner
with classification trees
Because of the host of variables and different scale levels,
O&MDC used a Chi-square-based (CHAID) segmentation
method. The CHAID procedure is currently one of four powerful
decision-tree algorithms in SPSS Classification Trees.
Present results in an intuitive manner—
perfect for non-technical audiences
Save information from trees as new
variables in data (information such
as terminal node number, predicted
value, and predicted probabilities)
The results gave UNICEF Germany a clearer understanding
of its donors. Consequently, UNICEF Germany was able to:
■ Increase direct mail response rate up to 80 percent
■ Raise the return on investment more than 65 percent
■ Target donors who are partial to the featured topic, thus
ensuring a better distribution among UNICEF donors
■ Decrease mailing volume dramatically without affecting
revenue from donations
Choose from CHAID, Exhaustive
CHAID, C&RT, and QUEST to find
the best fit for your data
Product specifications
Key features
■ Create tree-based classification
models for:
– Segmentation
– Stratification
– Prediction
– Data reduction and
variable screening
– Interaction identification
– Category merging and
discretizing continuous variables
Classify cases into groups or
predict values of a dependent
(target) variable based on values of
independent (predictor) variables
Validation tools for exploratory and
confirmatory classification analysis
View nodes using one of several
ways: Show bar charts of your
target variables, tables, or both
in each node
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Collapse and expand branches
without deleting the model
■ Generate syntax automatically
from the UI
■ Re-run tree building using syntax
in production mode
■ Score data based on results or
use results in further analysis
using other SPSS procedures
■ Four powerful tree modeling
– CHAID by Kass (1980)
– Exhaustive CHAID by Biggs,
de Ville and Suen (1991)
– Classification & Regression Trees
(C&RT) by Breiman, Friedman,
Olshen and Stone (1984)
– QUEST by Loh and Shih (1997)
■ Evaluation graphs enable
visual representation of gains
summary tables
■ Misclassification functionality
Gains chart: Identify segments by
highest (and lowest) contribution
■ Export output objects to any of
SPSS’ available output formats
■ Generate rules that define selected
segments in SQL to score databases
or SPSS syntax to score SPSS files
■ Export XML models to score cases
using the scoring engine feature in
SPSS Server (version 13.0 and
Get native access to database
management systems including
Oracle, SQL Server, DB2; additional
access to any ODBC-compliant
sources using the ODBC Wizard
System requirements
■ SPSS 15.0 for Windows
For more details and complete specifications, go to
Uncover patterns in your data with powerful tree-growing algorithms
The four algorithms in this add-on module differ from
more traditional statistics, such as logistic regression,
because these algorithms produce trees that enable
you to explore your results and visually determine how
your model flows. SPSS Classification Trees makes it
easier to identify specific subgroups and relationships
in your data than if more traditional statistics were used.
It breaks your data into branches and nodes, so you can
easily see where a group splits and terminates.
SPSS Classification Trees includes four established
tree-growing algorithms. Find the best fit for your data
by trying different algorithms or let SPSS Classification
Trees suggest the most appropriate algorithm.
■ CHAID — A statistical multi-way tree algorithm
that explores data quickly and builds segments and
profiles with respect to the desired outcome
■ Exhaustive CHAID — A modification of CHAID
that examines all possible splits for each predictor
(independent) variable
■ Classification & Regression Trees (C&RT) — A
complete binary tree algorithm, which partitions
data and produces accurate homogeneous subsets
■ QUEST — A statistical algorithm that selects variables
without bias and builds accurate binary trees quickly
and efficiently
Find the best fit for your data
Use highly visual trees to discover
relationships in your data
Choose from the following algorithms: CHAID,
Exhaustive CHAID, C&RT, and QUEST in SPSS.
Apply results to your data
Once you have produced your classification tree(s), you
can dig deeper into your data and gain more insight by
identifying a particular subset of the data via the tree,
and then run further analysis on this group.
SPSS Classification Trees makes it easy to interpret
results with diagrams, tables, and graphs. By producing
classification trees, you can discover relationships that
are currently hidden in your data and add annotations
for more detailed descriptions.
Segment and group cases directly within the data
Since you are creating classification trees directly within
SPSS, you can use the results to segment and group
cases directly within the data—without ever leaving
the SPSS environment. Additionally, you can generate
selection or classification/prediction rules in the form
of SPSS syntax, SQL statements, or simple text. Display
these rules in the Viewer and save them to an external
file for later use to make predictions about individual
and new cases.
Directly select cases or
assign predictions in
SPSS based on the model
results, or export rules for
later use.
Score cases directly in SPSS using the tree model results.
For more details and complete specifications, go to
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Get a clearer view of what your
data holds with complex sampling
SPSS Complex Samples 15.0
Achieve more statistically valid inferences for populations
Get more accurate results from your survey data
Analyze data and interpret results easily
marketing manager wants to know whether big-ticket
customers (organizations that spend more than $100,000)
are more satisfied than smaller-value customers. To uncover this
information, it was decided to survey the customer database and
build a model to predict customer satisfaction. But to implement a
survey to the entire database would not be cost effective. Instead,
1,000 customers were chosen to be the sample of the population.
Correctly and easily compute statistics
for complex samples
If a simple random sample of 1,000 customers was pulled, not
enough big-ticket customers would be provided to build a
reliable model, as big-ticket customers tend to be rare. Due to
the variability of characteristics, it is necessary to apply scientific
sample designs in the sample selection process to reduce the risk
of a distorted view of the population.
If you’re working with complex sample designs, such as stratified, clustered, or
multistage sampling, you need specialized statistical techniques to account for
the sample design and its associated standard error. The SPSS Complex Samples
add-on module for SPSS for Windows gives you everything you need for working
with complex samples—from the planning stage and sampling through to the
analysis stage.
To build an accurate predictive model for the complex sample,
the manager used SPSS Complex Samples. It was necessary to
stratify the customers into big-ticket and smaller-value customer
groups. The manager then drew a random sample of 500 within
each of these groups to conduct the survey.
SPSS Complex Samples helps you:
Get a more accurate picture of your data when working with large-scale surveys
Achieve more statistically valid inferences for populations
Reach correct point estimates for statistics such as totals, means, and ratios, and
obtain standard errors of these statistics
Predict numerical and categorical outcomes from non-simple random samples
Take up to three stages into account when analyzing data from a multistage design
Product specifications
Symbol indicates a new feature.
Complex Samples Plan (CSPLAN)
Provides a common place to specify
the sampling frame to create a
complex sample design or analysis
design used by procedures in SPSS
Complex Samples. To sample cases,
use a sample design created by
CSPLAN as input to the CSSELECT
procedure. To analyze sample
data, use an analysis design
created by CSPLAN as input to the
Complex Samples Selection
complex, probability-based samples
from a population. It chooses units
according to a sample design created
through the CSPLAN procedure.
Complex Samples Descriptives
estimates means, sums, and ratios,
and computes their standard errors,
design effects, confidence intervals,
and hypothesis tests for samples
drawn by complex sampling methods
Complex Samples Tabulate
displays one-way frequency tables
or two-way crosstabulations and
associated standard errors, design
effects, confidence intervals, and
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hypothesis tests for samples drawn
by complex sampling methods
Complex Samples General Linear
Model (CSGLM) — Enables you to
build linear regression, analysis
of variance (ANOVA), and analysis
of covariance (ANCOVA) models
for samples drawn using complex
sampling methods
■ Main effect, all n-way interactions,
and fully crossed and custom,
including nested terms
■ Model parameters: Coefficient
estimates, standard error for each
coefficient estimate, t test for each
coefficient estimate, confidence
interval for each coefficient estimate, design effect for each coefficient estimate, and square root
SPSS Complex Samples allowed the marketing manager to produce
a more accurate model to predict customer satisfaction. As a result,
the organization was able to make better business decisions.
of the design effect for each coefficient estimate
■ Population means of dependent
variable and covariates
■ Model fit
■ Test statistics: Wald F test, adjusted
Wald F test, Wald Chi-square test,
and adjusted Wald Chi-square test
■ Adjustment for multiple comparisons: Least significant difference,
Bonferroni, sequential Bonferroni,
Sidak, and sequential Sidak
■ Sampling degrees of freedom:
Sample design or fixed by user
■ Estimated means: Requests estimated marginal means for factors
and interactions in the model
■ Contrasts: Simple, deviation,
Helmert, repeated, or polynomial
Model variables can be saved to
the active file and/or exported to
external files that contain parameter matrices: Variables-predicted
values and residuals, parameter
covariance matrix, and its other
statistics, as well as parameter
correlation matrix, and its other statistics, can be exported as an SPSS
data file; parameter estimates and/
or the parameter covariance matrix
can be exported to an XML file
■ Sample design information
■ Regression coefficient estimates
and t tests
■ Summary information on dependent
variable, covariates, and factors
■ Summary information about the
sample, including the unweighted
count and population size
Confidence limits for parameter
estimates and user-specified confidence levels: Wald F test for model
effects, design effects, multiple R2,
set of contrast coefficients (L) matrices, variance-covariance matrix of
regression coefficient estimates,
root mean square error, and
covariance and correlation matrices
for regression coefficients
Complex Samples Ordinal
regression analysis on a binary or
ordinal polytomous dependent variable using the selected cumulative
link function for samples drawn by
complex sampling methods
■ Models: Same as CSGLM
For more details and complete specifications, go to
What’s New in
SPSS Complex
Samples 15.0
Incorporate complex sample designs into your data analysis
Only SPSS Complex Samples
makes understanding and working
with your complex sample
survey results easy. It is one of
the most comprehensive software
programs available.
SPSS Complex Samples provides
you with everything you need to
produce more accurate results…
■ Logistic regression: Predict
categorical outcomes (such as:
Who is most likely to buy my
product?) while taking the sample
design into account to accurately
identify groups
■ Ordinal regression: See sidebar
■ General linear models: Predict
numerical outcomes while taking
the sample design into account
■ Intuitive Sampling Wizard:
Guides you step-by-step through
the process of designing and
drawing a sample
■ Easy-to-use Analysis Preparation
Wizard: Helps prepare public-use
datasets for analysis, such as the
National Health Inventory Survey
Model parameters: Same as CSGLM
plus exponentiated estimates,
covariances of parameter
estimates, and correlations of the
parameter estimates
Model fit: Pseudo R2 and
classification table
– Parallel lines tests: Wald tests
of equal slopes, parameter estimates for generalized (unequal
slopes) model, and covariances
of parameter estimates for generalized (unequal slopes) model
– Summary statistics for model
Test statistics: Same as CSGLM
Adjustment for multiple
comparisons: Same as CSGLM
Sampling degrees of freedom:
Same as CSGLM
data from the Centers for Disease
Control and Prevention (CDC)
Easier collaboration with
colleagues: Easily share
sampling and analysis plans
More accurate analyses: Enables
you to take up to three stages
into account when analyzing data
from a multistage design
A more precise picture of
your data: Unlike traditional
statistics, subpopulation
assessments take other
subpopulations into account
Use the following types of sample
design information with SPSS
Complex Samples:
■ Stratified sampling: Increase
the precision of your sample or
ensure a representative sample
from key groups by choosing to
sample within subgroups of the
survey population. For example,
subgroups might be a specific
number of males or females or
contain people in certain job
categories, people of a certain
age group, and so on.
Model variable: Save predicted
category, probability of predicted
category, probability of observed
category, cumulative probabilities
(one variable per category), and
predicted probabilities (one
variable per category).
Export as an SPSS data file
Export as XML
Three estimation methods:
Newton-Raphson, Fisher Scoring,
and Fisher Scoring followed by
Cumulative link functions:
Cauchit, complementary log-log,
logit, negative log-log, and probit
Cumulative odds ratios for the
specified factor(s) or covariate(s)
Sample design information
Clustered sampling: Select
clusters, which are groups of
sampling units, for your survey.
Clusters can include schools,
hospitals or geographic areas
with sampling units that might
be students, patients, or citizens.
Clustering often helps make
surveys more cost-effective.
Multistage sampling: Select
an initial or first-stage sample
Accurate analysis of survey data
Sampling Plan
For more details and complete specifications, go to
Preparation Wizard
When collecting data:
n Specify the sampling
n Draw the sample
Design effects
Classification table
■ Set of contrast coefficients (L)
■ Variance-covariance matrix of
regression coefficient estimates
■ General estimable function table
■ Correlation matrix for regression
■ User-specified denominator, df,
used in computing p values for all
test statistics
■ Collinearity diagnostics
■ Fits model for a subpopulation
Complex Samples Logistic Regression
(CSLOGISTIC) — Performs binary
logistic regression analysis, as well
as multinomial logistic regression
(MLR) analysis, for samples drawn by
based on groups of elements in
the population; then create a
second-stage sample by drawing
a sub-sample from each selected
unit in the first-stage sample. By
repeating this option, you can
select a higher-stage sample. For
example, in a face-to-face survey,
you might sample individuals
within households and city blocks.
When working with public-use
n Specify how the sample was drawn
Save and share with colleagues
Plan files
Analyze data
complex sampling methods
■ Models: Same as CSGLM
■ Model parameters: Same as CSGLM
plus covariances of parameter
estimates, and correlations of the
parameter estimates
■ Model fit: Pseudo R and
classification table
■ Test statistics: Same as CSGLM
■ Adjustment for multiple comparisons: Same as CSGLM
■ Sampling degrees of freedom:
Same as CSGLM
■ Model variables can be saved
to the active file and/or exported
to external files that contain
parameter matrices
– Variables: Predicted category and
predicted probabilities
Descriptives—Analyze measures
of continuous types, including ratios
Tabulate—Analyze measures of
categorical types, including crosstabs
Complex samples general linear model—
Predict numerical outcomes
Complex samples ordinal regression—
Predict ordinal outcomes
Complex samples logistic regression—
Predict categorical outcomes
Accurately predict ordinal
outcomes from your complex
sample design by using complex
samples ordinal regression.
For example, accurately predict
customer satisfaction (low,
medium, or high) when using
sample survey methodology.
Select from two simple random
sampling (SRS) estimators in the
Complex Samples Plan (CSPLAN)
■ WOR (without replacement) SRS
variance estimator includes the
finite population correction. This
estimator is the default.
■ WR (with replacement) SRS
variance does not include the
of survey
data This
is easy
estimator is recommended when
Start with one of the wizards
have been
on your
not add up
interface to create plans, analyze
data and interpret results.
– Parameter covariance matrix
and its other statistics, as well
as parameter correlation matrix
and its other statistics, can be
exported as an SPSS data file
– Parameter estimates and/or the
parameter covariance matrix can
be exported to an XML file
■ Sample design information
■ Summary information about the
dependent variable, covariates,
and factors
■ Sample summary information
■ Confidence limits for parameter
estimates and user-specified
confidence levels
■ Model summary statistics
■ Wald F test for model effects
■ Design effects
Classification table
Set of contrast coefficients (L)
■ Variance-covariance matrix of
regression coefficient estimates
■ Root mean square error
■ Covariance and correlation matrices
for regression coefficients
■ User-specified denominator, df,
used in computing p values for all
test statistics
■ Collinearity diagnostics
■ Models can be fit for
System requirements
■ SPSS 15.0 for Windows
■ Other system requirements vary
according to platform
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Conjoint analysis helps identify
customer preferences
SPSS Conjoint 15.0
Identify product features important to new customers
Discover which product attributes are most important
to current customers
Determine the influence product attributes have on
customer preferences
Discover what drives your customers’
purchase decisions
Thoroughly understand consumer preferences, tradeoffs, and price sensitivity with
SPSS Conjoint. By using conjoint analysis, you can uncover more information about
how customers compare products in the marketplace and measure how individual
product attributes affect consumer behavior. Armed with this knowledge, you can
design, price, and market products and services tailored to your customers’ needs.
SPSS Conjoint 15.0 outputs its results to pivot tables, which:
Provide better, more professionallooking and presentable charts
■ Offer more formatting options than
text output
■ Can be exported to Microsoft
Word, Excel, or PowerPoint
■ Can be used by the SPSS Output
Management System (OMS)
software firm planned to develop training programs in
addition to the traditional instructor-led training.
Since many options were available, they decided to perform
a conjoint study to evaluate the proposed product.
Six key attributes believed to influence consumer preference were
identified: method of delivery, video content, types of examples,
certification test, method of asking questions remotely, and price.
Four of these attributes have two levels, while the other two
have three. The resulting full-factorial design would have 144
alternative product bundles (2 x 2 x 2 x 2 x 3 x 3), making for an
unreasonably large study. However, using the Orthoplan process
in SPSS Conjoint 15.0, the researcher can reduce this down to
only 16 hypothetical product bundles (such as those in Figure 1)
while ensuring that they will still receive all the information needed
to perform a complete analysis. Then these 16 bundles can be
printed with Plancards and given to a sample of target users to rank.
When these preference rankings are analyzed with SPSS Conjoint,
the results shown in Figure 2 are produced. Two attributes stand
out as very important—inclusion of video and price—while the
certification test and types of examples are relatively unimportant.
The Utility and Factor columns in Figure 2 show the relative
preference for each level of each attribute. Within question
method, instant messaging is the most preferred and no support is
the least preferred.
SPSS Conjoint helps uncover answers
to your critical questions:
Which features or attributes of a product or service drive the purchase decision?
Which feature combinations will have the most success?
What market segment is most interested in the product?
What marketing messages will most appeal to that segment?
What is the optimal price to charge consumers for a product or service?
Who are the closest competitors in the market?
Figure 1
Figure 2
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For more details and complete specifications, go to
How does your product rank?
SPSS Conjoint provides the tools you need to find out
SPSS Conjoint offers the procedures you need to
plan, implement, and analyze efficient conjoint
surveys. With these techniques, you can discover
how respondents rank their preferences and
product attributes.
Orthoplan — Produces an orthogonal array of
product attribute combinations, dramatically
reducing the number of questions you must ask
while ensuring enough information to perform a
full analysis.
Plancards — Print “cards” to elicit respondents’
preferences. Quickly generate cards that respondents can sort to rank alternative products.
Conjoint procedure — Get results you can act on,
such as which product attributes are important
and at what levels they are most preferred. Plus,
perform simulations which will tell you expected
market shares for alternative products.
Save time and money with Orthoplan
Establish the parameters of your study with the Orthoplan design
generator. Orthoplan gives you an orthogonal array of alternative
potential products that combine different product features at
specified levels. This saves you time and money by presenting a
fraction of all possible alternatives.
Rank preferences
Conjoint quickly guides you through creating “plan cards” that
respondents sort to rank their preferences of alternative products.
Generate charts
After you gather and input the data using your
plan cards, SPSS Conjoint performs an ordinary
least squares analysis of preference or rating
data. It then generates charts to simulate
expected market shares. For instance, in this
chart you can quickly see that subjects strongly
consider package design and brand name as
the most important.
Add SPSS Conjoint to your competitive research
and develop products and services that are more
Product specifications
Orthoplan — Generates orthogonal
main effects fractional factorial
designs; it is not limited to twolevel factors
■ Specify the desired number of
cards for the plan; Orthoplan will
try to generate a plan in the desired
minimum number of runs
■ Generate holdout cards to test the
fitted conjoint model
■ Orthoplan can mix the training and
holdout cards or can stack the holdout cards after the training cards
■ Save the plan file as an SPSS
system file
■ Pivot table output
Plancards — A utility procedure
used to produce printed cards for
a conjoint experiment; the printed
cards are used as stimuli to be sorted,
ranked, or rated by the subjects
■ Specify the variables to be used as
factors and the order in which their
labels are to appear in the output
■ Choose a format
– Listing-file format: Differentiate
holdout cards from experimental
cards and list simulation cards
separately following the
experimental and holdout cards
– Card format: Holdout cards are
not differentiated and simulation
cards are not produced
■ Write the cards to an external file
or the listing file
Specify optional title and footer
Pivot table output
Conjoint — Performs an ordinary
least squares analysis of preference
or rating data
■ Work with the plan file generated
by Plancards or a plan file input by
the user using DATA LIST
■ Work with individual level rank or
rating data
■ Provide individual level and
aggregate results
■ Treat the factors in any of a number
of ways; conjoint indicates reversals
– Discrete: Factor levels
are categorical
– Linear: Scores or ranks are
linearly related to the factor
For more details and complete specifications, go to
– Ideal: A quadratic relationship
is expected between the scores
or ranks and the factor; this
method assumes that there is
an ideal level for the factor, and
that distance from the ideal point
in either direction is associated
with decreasing preference
– Antideal: A quadratic
relationship is expected between
the scores or ranks and the
factor; this method assumes
that there is a worst level for
the factor, and that distance
from this point in either
direction is associated with
increasing preference
Experimental cards have one
of three scenarios: Training,
holdout, and simulation
Three conjoint simulation
methods: Max utility; BradleyTerry-Luce (BTL); and logit
Print control
– Print the results of the
experimental (training and
holdout) data analysis
– Print the results of the
conjoint simulation
Write utilities to an external file
Print results
– Attribute importance
– Utility (part-worth) and
standard error
– Graphical indication of most
to least preferred levels
of each attribute
– Counts of reversals and reversal
summary Pearson R for training
and holdout data
– Kendall’s tau for training and
holdout data simulation results
and simulation summary
Pivot table output
System requirements
■ SPSS 15.0 for Windows
■ Other system requirements
vary according to platform
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Benefits of using
SPSS Data Preparation:
SPSS Data Preparation 15.0
Quickly identify invalid cases so you can inspect
them prior to analysis
Formerly known as SPSS Data Validation™
Streamline the data preparation process
Eliminate labor-intensive manual checks
Reach more accurate conclusions
Eliminate labor-intensive manual checks by
performing automatic data checks based on each
variable’s measure level, categorical or continuous
Prevent outliers from skewing a predictive model
using anomaly detection prior to model building
Improve data preparation for
more accurate results
Quickly pre-process your data to get it ready
for analysis
What’s New in
SPSS Data Preparation 15.0
This add-on module enables you to easily identify suspicious and invalid cases,
variables, and data values; view patterns of missing data; and summarize variable
distributions. You can streamline the data preparation process so that you can get
ready for analysis faster and reach more accurate conclusions.
Reach the best possible outcome with the new Optimal
Binning procedure. Now you can more accurately use
algorithms designed for nominal attributes (such as Naïve
Bayes and logit models). Optimal Binning enables you to
bin, or set cutpoints for, scale variables.
Perform automatic data checks
Eliminate time-consuming, tedious manual checks by using the Validate Data
procedure. This procedure enables you to apply rules to perform data checks
based on each variable’s measure level (whether categorical or continuous). Then,
determine data validity and remove or correct suspicious cases at your discretion
prior to analysis.
Select from three types of optimal binning for
preprocessing data prior to model building:
■ Unsupervised: Create bins with equal counts
■ Supervised: Take the target variable into account
to determine cutpoints. This method is more
accurate than unsupervised; however, it is also more
computationally intensive.
■ Hybrid approach: Combines the unsupervised and
supervised approaches. This method is particularly
useful if you have a large amount of distinct values.
Quickly find multivariate outliers
Easily detect multivariate outliers so you can further examine them and determine if
they should be included in your analyses. The Anomaly Detection procedure searches
for unusual cases based upon deviation. It enables you to flag outliers by creating a
new variable.
Product specifications
Symbol indicates a new feature.
Validate data
Use the Validate Data procedure to
validate data in the working data file
■ Basic checks:
– Maximum percent of missing
values, single category cases,
and cases with a count of 1
– Minimum coefficient of variation
– Minimum standard deviation
– Flag incomplete IDs, duplicate
IDs, and empty cases
Standard rules: Describe the data,
view single variable rules, and
apply them to analysis variables
– Description of data:
■ Distribution: Shows a
thumbnail-size bar chart for
categorical variables or
histogram for scale variables
■ Min./max. data values shown
– Single-variable rules:
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Apply rules to identify
missing or invalid values
■ User-defined
Custom rules: Define crossvariable rule expressions in which
respondents’ answers violate logic
Output: Reports for invalid data
– Casewise report, specify by case
■ Specify the minimum number
of violations needed for a case
■ Specify the maximum number
of cases in the report
– Standard validation rules reports
Summarize violations by
analysis variable and rule
■ Display descriptive statistics
Save: Save variables that record
rule violations and use them to
clean data and filter out bad cases
– Summary variables:
■ Empty case indicator
■ Duplicate ID indicator
■ Incomplete ID indicator
■ Validation rule violation
– Indicator variables that record all
validation rule violations
Identify unusual cases
Anomaly Detection procedure.
Search for unusual cases, based
upon deviations from peer group,
and reasons for deviations
■ VARIABLES subcommand: Specify
categorical, continuous, and ID
variables, and list variables that
are excluded from the analysis.
■ HANDLEMISSING subcommand:
Specifies the methods of handling
missing values in this procedure
■ The CRITERIA subcommand
specifies the following settings:
– Number of peer groups
– Adjustment weight on the
measurement level
– Number of reasons in the
anomaly list
– Percentage and number of cases
considered as anomalies and
included in the anomaly list
– Cutpoint of the anomaly index to
determine whether a case is
considered as an anomaly
Save additional variables to the
For more details and complete specifications, go to
Identify suspicious or invalid cases, variables, and data values easily
with SPSS Data Preparation
Basic checks: You can specify basic checks to apply
to variables and cases in your file. For example, you
can obtain reports that identify variables with a high
percentage of missing values or empty cases.
Variables tab: The Validate Data dialog is used to
validate your data. The Variables tab shows variables in
your file. Start by selecting the variables you are interested
in and moving them to the Analysis Variables list.
Save: You can also save
variables that record
rule violations and can
be used to help you clean
your data and filter out
bad cases.
Standard rules: Apply rules to individual variables that
identify invalid values such as values outside a valid
range or missing values.
When you press OK, the
Validate Data dialog produces
reports that summarize
invalid values and cases.
Define custom rules: Create cross-variable rules
in which respondents’ answers violate logic
(such as “pregnant males”).
Define standard rules: The Validate Data
dialog lets you create your own rules or apply
predefined rules.
working data file including:
– Anomaly index
– Peer group ID, size, and size
in percentage
– Variable, variable impact measure, variable value, and norm
value associated with a reason
OUTFILE subcommand: Write a
model to a filename as XML
PRINT subcommand prints:
– Case-processing summary
– Anomaly index list, anomaly peer
ID list, and anomaly reason list
– The Continuous Variable Norms
table, for continuous variable,
and the Categorical Variable
Norms, for categorical variable
– Anomaly Index Summary
– Reason Summary Table
Optimal Binning
Preprocess data with Optimal
Binning. Categorizes one or more
continuous variables by distributing
the values of each into bins.
Select from the following methods:
– Unsupervised binning via the
equal frequency algorithm. It
uses the equal frequency algorithm to discretize the binning
input variables. Guide variable
not required.
– Supervised binning via the MDLP
(Minimal Description Length
Principle) algorithm. Discretizes
binning input variables using the
MDLP algorithm without any pre-
For more details and complete specifications, go to
processing. Ideal for small datasets. Guide variable required.
– Hybrid MDLP binning. Involves
preprocessing via the equal
frequency algorithm, followed
by the MDLP algorithm. Ideal for
large datasets. Guide variable
Specify the following criteria:
– How to define the minimum
and maximum cutpoint for each
binning input variable, and the
lower limit of an interval
– Whether to force-merge sparsely
populated bins
– Whether missing values uses
listwise or pairwise deletion
Save new variables with binned
values and syntax to an SPSS
syntax file
PRINT subcommand prints:
– The binning input variables’
cutpoint sets
– Descriptive information for all
binning input variables
– Model entropy for binned
System requirements
■ SPSS 15.0 for Windows
■ Other system requirements vary
according to platform
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SPSS Exact Tests is crucial
to your research if you are:
SPSS Exact Tests 15.0
“What impresses me
most about SPSS
Exact Tests is how it
fills the deficiencies
that many of the major
statistics programs
have in the area of
nonparametric inference
and hypothesis tests...”
– Vincent C. Arena, Ph.D,
University of Pittsburgh,
Graduate School of
Public Health Department
of Biostatistics
Product specifications
Succeeds where traditional tests fail
Offers more than 30 exact tests, so you can analyze rare
occurrences in large datasets
Ensures you’ll work more confidently with smaller sample sizes
Reach accurate conclusions with small
samples or rare occurrences
SPSS Exact Tests is the add-on module
to turn to when you’d like to analyze rare
occurrences in large databases or work
more accurately with small samples. With
more than 30 exact tests, you’ll be able to
analyze your data where traditional tests
fail. With SPSS Exact Tests, you can use
smaller sample sizes and be confident of
your results.
SPSS Exact Tests has the tests and
statistics you need, including:
■ Exact p-values
■ Monte carlo p-values
■ Pearson Chi-squared test
■ Linear-by-linear association test
Linear-by-linear association test
Exact 1-tailed and 2-tailed p-values
and exact point probability
■ Monte Carlo 1-tailed and
2-tailed p-values and CIs
Tests and statistics
Pearson Chi-square test, Likelihood
ratio test, and Fisher’s Exact test
■ Exact 1-tailed, 2-tailed p-values
for 2x2 table
■ Exact 2-tailed p-values for
general RxC table
■ Monte Carlo 2-tailed p-value
and confidence intervals for
general RxC table
Operating with a small number of cases
Working with variables that have a high percentage
of response in one category
Subsetting your data into fine breakdowns
Searching for rare occurrences in large datasets
Contingency coefficient, Phi,
Cramer’s V, Goodman and Kruskal
Tau, Uncertainty coefficient—
symmetric or asymmetric, Kappa,
Gamma, Kendall’s Tau-b and Tau-c,
Somer’s D—symmetric and
asymmetric, Pearson’s R, and
Spearman correlation
■ Exact 2-tailed p-value
■ Monte Carlo 2-tailed p-value
and CIs
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Contingency coefficient
Uncertainty coefficient—symmetric
or asymmetric
Wilcoxon signed-rank test
Cochran’s Q test
Binomial test
Analyze small
datasets and get
more precise results
In this example,
even though there
are only 10 cases,
SPSS Exact Tests
helps you determine
that a significant
relationship exists.
SPSS Exact Tests ensures you’ll always
have the right statistical test for your data.
And because SPSS Exact Tests is part of
the SPSS integrated product line, you can
count on beginning-to-end solutions for
your modeling and data analysis needs.
McNemar test
■ Exact 1-tailed and 2-tailed
p-value and point probability
Sign test and Wilcoxon
signed-rank test
■ Exact 1-tailed and 2-tailed
p-value and point probability
■ Monte Carlo 1-tailed and
2-tailed p-values and CIs
Marginal homogeneity test
■ Asymptotic, exact, Monte Carlo
1-tailed and two 2-tailed
p-values, and point probability
Print both asymptotic and exact p-values
Note the exact p-values show statistical significance at the
.05 level or less, whereas the asymptotic p-values incorrectly
show nonsignificance.
2-Sample Kolmogorov-Smirnov test
■ Exact 2-tailed p-value, and
point probability
■ Monte Carlo 2-tailed p-values
and CIs
Wald-Wolfowitz Runs test
■ Exact 1-tailed p-value and
point probability
■ Monte Carlo 1-tailed p-value
and CIs
Mann-Whitney U or Wilcoxon
Rank-sum W test
■ Exact 2-tailed p-value and
exact 1-tailed p-value, and
point probability
■ Monte Carlo 1-tailed and
2-tailed p-value and CIs
Cochran’s Q test, Friedman test,
Kendall’s coefficient of concordance,
Kruskal-Wallis test, Median test,
1-Sample Kolmogorov-Smirnov
test and 1-Sample Wald-Wolfowitz
Runs test
■ Exact 2-tailed p-value and
point probability
■ Monte Carlo 2-tailed p-values
and CIs
Jonckheere-Terpstra test
■ Asymptotic, exact, Monte Carlo
1-tailed and 2-tailed p-values,
and point probability
Binomial test
Both exact 1-tailed and 2-tailed
p-values, and point probability
System requirements
SPSS 15.0 for Windows
■ Other system requirements
vary according to platform
For more details and complete specifications, go to
Four ways to expand your
analysis with SPSS Maps:
SPSS Maps 15.0
Visualize your analysis geographically
Pinpoint locations for profitable business sites
Determine where your most profitable customers are located
1. Analyze the relevance of geographic variables
2. Interpret sales data and determine where your most
profitable customers are located
3. Display sales trends, crime rates, or accident rates for
specific locations
4. Discover where prospects matching your customer
profiles are located
Chart a course for better decision making
SPSS Maps enables you to transform your
SPSS data into demographic information.
With SPSS Maps, you’ll make more informed
decisions because you have a clearer picture
of how geographic variables can affect
your organization. It integrates seamlessly
with SPSS menus to provide the capabilities
you need to easily turn your data into visual,
easy-to-read maps.
■ Powerful visualization map plots:
Add geographic analysis with dot density,
bar charts, pie charts, graduated symbols,
individual values box, and range of values
■ Multiple thematic layers: Combine
several thematic layers and customize
with photo background pictures for
professional-quality, realistic maps
■ Data binding: Automatically attach data
to your maps
Product specifications
Key features
■ Six thematic map options
– Dot density: Display a number
of dots for data points proportional to the area’s specific level;
points may be assigned manually
or let SPSS Maps plot points
– Bar: Display bar charts for each
selected geographic area
– Pie: Display pie charts for each
selected geographic area
– Graduated symbols: Display a
symbol whose size corresponds
to the relative level for a
geographic area
– Individual values box: Use color
or patterns to identify categories
for each area
– Range of values: Display color
shades for each geographic area
to represent relative value
Multiple thematic layers: Display
more than one thematic on a single
geographic map
Several sample maps/geosets:
Maps from all over the world are
provided in MapInfo format, such
as European capitals, demographics
for Asia, world top 25 cities, plus
much more
For more details and complete specifications, go to
Geoset manager: Quickly convert thirdparty maps into compatible data—
mismatches are automatically converted
Map summary statistics: Automatically
display map summary statistics for the entire
map or any selected or highlighted region
Automatic chart labeling: Eliminates tedious thematic key formation
Sample datasets: Includes demographic
data from Claritas Inc. for quick analysis,
and sample datasets from MapInfo®
for instant demographic comparisons
SPSS syntax editor: Offers one-click
access to production mapping
Flexible output: Export maps as
standard graphic files for distribution
almost anywhere, including the Web
Import maps: Import additional
maps for specialized requirements
Free sample datasets: SPSS Maps
is bundled with demographics,
Workplace Population, PRIZM® and
CLOUT™ data from Claritas Inc. Plus,
more than 40 international MapInfo
demographic data files help in
quick demographic analysis without laborious data collection
SPSS syntax programming:
Produce maps with standard
SPSS syntax
Automatic labeling: Any graph
used in presentation or publication
needs labels and a key. This
feature eliminates the need to
manually label key elements.
Undo and redo: Map creation/
editing/viewing include the
ability to eliminate or redo the
last editing step
Zoom and unzoom: Map
magnitude parameters are easy to
change during viewing for quick
orientation and exploration. Level
of features automatically changes
for proper level of detail.
Projections and coordinate system:
Coordinate systems and
projections can be displayed on the
created maps
Map formatting: Choose colors and
other aesthetic attributes
With SPSS Maps, you can
display, edit, and customize
your data to produce the best
map to present your message.
Map export tool: Export maps as
standard graphic files (WMF, CGM,
for distribution almost anywhere
Supported statistics
■ Medians
■ Modes
■ Variances
■ Maximum values
■ Minimum values
■ Standard deviations
■ First values
■ Last values
■ Sums
■ Number of cases less than
Number of cases greater than
Percentage of cases less than
Percentage of cases greater than
Number of cases less than
or equal to
Percentage of cases less
than or equal to
Number of cases greater
than or equal to
Percentage of cases greater
than or equal to
System requirements
SPSS 15.0 for Windows
■ Other system requirements
vary according to platform
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SPSS Missing Value Analysis 15.0
Overcome missing data issues
Reach more statistically significant results when taking missing values into account
Determine the extent of missing data quickly
Create higher-value data and build better models
when you estimate missing data
Even in the best-designed and monitored study or survey, observations can be missing—a person
inadvertently skips a question, a sample or response is illegible or there are technical malfunctions.
SPSS Missing Value Analysis allows you to quickly and easily diagnose your missing data and fill in
the blanks to create higher-value data which result in better models. When you ignore or exclude
missing data, you risk reaching invalid and insignificant results.
Don’t risk invalid results!
With SPSS Missing Value
Analysis 15.0, you can:
Diagnose if you have a serious
missing data problem
Replace missing values with estimates
Ensure you enter the data analysis
stage using data that takes missing
values into account
Improve survey questions—identify
possibly troublesome or confusing
questions, based on observed missing
data patterns
Draw more valid conclusions and
remove hidden bias from your data
Make SPSS Missing Value Analysis a part of your data management and preparation step, and you’ll
enter the data analysis stage using data that takes missing values into account.
Missing data: the hidden problem
consumer goods company’s primary source of customer information
is a few survey questions on the warranty card returned by customers.
The warranty card survey collects data on age, occupation, gender, marital
status, family size, and income. The marketing department analyzes the
data to better understand the demographics of their customer base to
more effectively target promotions.
They used SPSS Missing Value Analysis to investigate the extent of
missing data. First, they produced a summary table, which gave an
overview of the responses for each question. The question with the
highest rate of missing data is income (34%). Further investigation
reveals that 31 customers did not report occupation and income.
Missing data appears to be a potential problem.
SPSS Missing Value Analysis offers two methods for maximum
likelihood estimation and imputation—EM (Expectation-Maximization)
algorithm and regression. Both of these highly sophisticated methods
can be used to inspect the results. When used by the company, these
methods produced a different response than the data with missing values.
Compare the results to the data with missing values:
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Incomplete data (calculated with missing values)
Largest segment: 38% married women
■ Income range: $18,000 – $42,000
■ Occupation: any
Cost per piece: $1
100,000 names purchased
Total cost for mailing: $100,000
Complete data
■ Largest segment: 46% married women
■ Income range: $17,000 – $39,000
■ Occupation: non-professional
Incomplete data
Response rate: 2%
Total number of responses: 2,000
Closed sales: 1,000
Average revenue per sale: $100
Total revenue: $100,000
Profit: $0
While these differences may appear subtle, they translate into
significant cost savings. Consider the case where the marketing
department purchases a list for a direct mail promotion based on
these target market demographics. More people are mailed to that
are outside the target market, lowering the response rate. Overall,
the direct mail campaign based on complete data is more profitable.
Complete data
Response rate: 4%
Total number of responses: 4,000
Closed sales: 2,000
Average revenue per sale: $100
Total revenue: $200,000
For more details and complete specifications, go to
Draw more valid conclusions with
SPSS Missing Value Analysis
Product specifications
It’s easy for you to evaluate the affect of missing
data, especially with small datasets, because
SPSS Missing Value Analysis offers the features
and benefits below.
Discover missing data
Six tailor-made displays: Examine data from
several angles using six diagnostic reports to
uncover missing data patterns.
Diagnose missing data problems: Get a case-bycase overview of your data with the data patterns
report. By giving you a snapshot of each type of
missing value and any extreme values for each
case, you can determine the extent of missing
data quickly.
First, investigate where the missing values are located and how extensive they
are. This summary tables shows 31.2% of the cases are missing daily calorie
intake values.
Better summary statistics: Adjust for missing
values so you get a more accurate description
of your data. Choose from four methods:
listwise deletion, pairwise deletion, EM, and
covariance matrix.
Detect patterns in your data
Fill in missing data: Easily replace missing
values with estimates and increase your chance
of reaching statistically significant results.
Choose from the EM and regression algorithms
to predict missing values based on data you
already have.
Powerful and accurate analysis, with
all cases represented
Next, explore patterns of missing data. Here, the missing value
patterns show a large discrepancy in GDP. The GDP per capita is
$3,108 for the 59 countries with no missing data and $16,554 for the
15 counties where Literacy_Male and Literacy_Female are missing.
Analyze patterns
■ Display missing data and
extreme cases for all cases
and all variables using the
data patterns table
– Displays system missing and
three types of user-defined
missing values
– Sort in ascending or
descending order
– Display actual values for
specified variables
■ Display patterns of missing
values for all cases that have at
least one missing value using
the missing patterns table
– Group similar missing value
patterns together
– Sort by missing patterns and
■ Determine differences between
missing and non-missing
groups for a related variable
with the separate variance
t test table
■ Show differences between
present and missing data for
categorical variables using
the distribution of categorical
variables table
■ Assess how much missing
data for one variable relates
to the missing data of another
variable using the percent
mismatch of patterns table
– Sort matrices by missing
value patterns or variables
■ Identify all unique patterns
with the tabulated patterns
table, which summarizes each
missing data pattern and
displays the count for each
pattern plus means and
frequencies for each variable
– Display count and
averages for each missing
value pattern using
the summary of missing
value patterns table
Univariate: Compute count,
mean, standard deviation,
and standard error of mean
for all cases excluding those
containing missing values,
count and percent of missing
values, and extreme values
for all variables
■ Listwise: Compute mean,
covariance matrix, and
correlation matrix for all
quantitative variables for cases
excluding missing values
■ Pairwise: Compute frequency,
mean, variance, covariance
matrix, and correlation matrix
■ EM algorithm
– Estimate the means,
covariance matrix, and
correlation matrix of
quantitative variables with
missing values, assuming
normal distribution,
t-distribution with degrees
of freedom, or a mixednormal distribution with
any mixture proportion and
any standard deviation ratio
■ Regression algorithm
– Estimate the means, covariance matrix, and correlation
matrix of variables set as
dependent; set number of
predictor variables; and set
random elements as normal,
t, residuals, or none
Data management
■ Handle all character variables
as categorical variables
■ Use the first eight characters of
a string variable when defined
as categorical
■ Save the completed data
matrix as an external file
System requirements
■ SPSS 15.0 for Windows
■ Other system requirements
vary according to platform
Estimate means, standard deviations, covariances, and correlations
using listwise (complete cases only), pairwise, EM, and/or regression methods—so that all cases are represented in your analysis.
Here, the estimated means using the four estimation methods are
shown in a summary table.
For more details and complete specifications, go to
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Logistic regression helps target the
right customers and increase profits
SPSS Regression Models 15.0
Predict categorical outcomes with more than two categories
Easily classify your data into two groups
Gain more control over your model
Make better predictions with powerful
regression procedures
SPSS Regression Models, an add-on module for SPSS, gives you an even wider range
of statistics so you can get the most accurate response for specific data types. Do you
build predictive models but find ordinary least squares regression too limiting? If so,
SPSS Regression Models can make your life easier.
Use SPSS Regression Models for:
Market research: Study consumer buying habits
Medical research: Study response to dosages
Loan assessment: Analyze good and bad credit risks
Institutional research: Measure academic achievement tests
SPSS Regression Models is crucial
to your research if…
You need to predict a categorical outcome
The relationship between the outcome and a set
of predictors is thought to be nonlinear
Your data violates the assumptions of linear regression
What’s New in
SPSS Regression Models 15.0
Gain improved diagnostics in the classification table within the multinomial
logistic regression (MLR) procedure.
Determine how well you’ve classified data. Previously, you might have checked
your work by using crosstabs. With improved diagnostics, you can omit this step.
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mail-order bookstore is seeking to increase the average
dollar value of its orders. By increasing the size of each
order, the bookseller can increase profits significantly—especially
since their fixed costs are covered by the current product cost.
Since mailing to the entire customer database is costly, the
bookseller wants to identify customers most likely to accept the
offer. This way it can target future promotions specifically to these
customers (high-value) and yield a higher profit while incurring
moderate promotional costs. It did this by taking a closer look
at customers’ past transaction history and identifying if they
responded to past promotions driving a high-value purchase.
To identify potential high-value customers, the bookseller used the
technique, binary logistic regression, to model customer response
to the promotion. They found the following variables to have
significant value for predicting which customers were more likely
to respond to the promotion:
■ Total dollars spent on products in the previous year
■ Items purchased for children in the past year (none, under $30,
more than $30)
■ Number of bestsellers purchased in the past three months (none,
one, or more than one)
These variables formed the basis for a model that the bookseller
used to score each customer in its database for likelihood
to respond to a promotion. The bookseller used these scores
to rank customers and identify the best prospects for the
promotion. Then they could target a mailing to these individuals.
In addition, management was able to review the findings and
discovered that customers who made purchases in the children’s
category and those who purchased several bestsellers in a short
period of time were particularly good prospects for large purchases.
By gaining better insight into their customers’ behaviors and needs,
they could more accurately plan and target future ads and promotions.
For more details and complete specifications, go to
Find the best predictor from dozens of possibilities
Apply more sophisticated models with SPSS Regression
Models’ wide range of nonlinear modeling procedures.
■ Multinomial logistic regression (MLR): Predict categorical
outcomes with more than two categories. Free of
constraints such as yes/no answers, MLR allows you to
model which factor predicts if the customer buys product
A, B, or C.
– Stepwise function in MLR: Save time and easily find
the best predictors for your data
Choose from four methods for choosing predictors:
forward entry, backward elimination, forward
stepwise, and backward stepwise
Use Score and Wald methods for a faster and more
accurate conclusion for variable selection
– Apply a highly scalable, high-performance
algorithm to handle big datasets
– Save time by specifying the reference category in your
outcome variable in the user interface. You no longer
need to recode the dependent variable set up in the
desired reference category.
– Use Akaike Information Criterion (AIC) and Bayesian
Information Criterion (BIC) to better assess model fit
■ Binary logistic regression (BLR): Predict dichotomous
variables such as buy or not buy, vote or not vote. This
procedure offers many stepwise methods to select
the main and interaction effects that best predict your
response variable.
Nonlinear regression (NLR) and constrained nonlinear
regression (CNLR): Get control over your model and
your model expression. These procedures give you two
methods for estimating parameters of non-linear models.
Weighted least square regression (WLS): Give more
weight to measurements within a series
Probit analysis (PROBIT): Analyze potency of responses
to stimuli, such as medicine doses, prices, or incentives.
Probit evaluates the value of the stimuli using a logit
or probit transformation of the proportion responding.
The multinomial logistic regression procedure predicts a categorical
outcome such as “primary reason for Web use.” The categories shown
in this example are: a) work only, b) shopping only, c) both working and
shopping, and d) neither (reference category). Search engine use was a
better predictor of “shopping only” than print media use. Search engine
users were 1.837 times more likely to use the Web for “shopping only”
purposes than were non-users of search engines.
Powerful regression procedures at work in the wireless industry
Build predictive models
Predict the presence or
absence of a characteristic/
outcome based on values of
a set of predictor variables.
In this example, a wireless
telephone service provider
is interested in identifying
dissatisfied customers so
they can intervene before
they defect and switch to
a competitor.
For more details and complete specifications, go to
Predict categorical outcomes
Use the binary logistic regression procedure to
test the impact of service offers (new or expanded)
on customer satisfaction. The classification table
indicates that this model can correctly predict
satisfaction with 90% accuracy.
Product specifications
Symbol indicates a new feature.
Multinomial logistic regression
■ Control the values of the algorithm-tuning parameters using
the CRITERIA subcommand
■ Include interaction terms
■ Customize hypotheses
by directly specifying
null hypotheses as linear
combinations of parameters
using the TEST subcommand
■ Specify a dispersion scaling
value with SCALE subcommand
■ Build equations with or without
a constant
■ Use a confidence interval for
odds ratios
■ Save the following statistics:
Predicted probability, predicted
response category, probability
of the predicted response
category, and probability of the
actual response category
■ Find the best predictor from
dozens of possible predictors
using stepwise functionality
■ Use Score and Wald methods,
to quickly reach results with a
large number of predictors
■ Assess model fit using Akaike
information criterion (AIC) and
Bayesian information criterion (BIC; also called Schwarz
Bayesian Criterion, or SBC)
■ Diagnostics for the
classification table:
– Percent concordance
– Percent ties
– Percent discordance
– C-value for logistic mode
– Somer’s D
– Gamma
– Tau-a statistics
Binary logistic regression (BLR)
■ Forward/backward stepwise
and forced entry modeling
■ Transform categorical variables
by using deviation contrasts,
simple comparison, difference
(reverse Helmert) contrasts,
Helmert contrasts, polynomial
contrasts, comparison of adjacent categories, user-defined
contrasts, or indicator variables
■ Criteria for model building:
Probability of score statistic
for entry, probability of Wald,
or likelihood ratio statistic
for removal
Save the following statistics:
Predicted probability and
group, residuals, deviance
values, logit, Studentized
and standardized residuals,
leverage value, analog of
Cook’s influence statistic, and
difference in Beta
■ Export the model using XML
Constrained nonlinear regression
■ Save predicted values,
residuals, and derivatives
■ Choose numerical or userspecified derivatives
Nonlinear regression (NLR)
■ Specify loss function options
■ Use bootstrap estimates of
standard errors
Weighted least squares (WLS)
■ Calculate weights based
on source variable and
Delta values or apply from an
existing series
■ Output for calculated weights:
Log-likelihood functions for
each value of Delta; R, R2,
adjusted R2, standard errors,
analysis of variance, and t
tests of individual coefficient
for Delta value with maximized
log-likelihood function
■ Display output in pivot tables
Two-stage least squares (2SLS)
■ Structural equations and
instrumental variables
■ Control for correlations
between predictor variables
and error terms
■ Display output in pivot tables
■ Transform predictors: Base 10,
natural, or user-specified base
■ Natural response rate
estimates or specified
■ Algorithm control parameters:
Convergence, iteration limit,
and heterogeneity criterion
■ Statistics: Frequencies, fiducial
confidence intervals, relative
median potency, test of parallelism, plots of observed probits, or logits
■ Display output in pivot tables
System requirements
■ SPSS 15.0 for Windows
■ Other system requirements
vary according to platform
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Maximize your time with
SPSS Tables
SPSS Tables 15.0
Quickly create tables with a drag-and-drop interface
Preview tables as you create them to get it right the first time
Customize tables to make it easier for your audience to understand
Create custom tables in no time
SPSS Tables, the add-on reporting module for SPSS for Windows, enables you to
turn your analysis into high-quality tabular reports. Easily display your data analysis
in presentation-quality, production-ready tables.
With SPSS Tables, you have the features
you need to easily create and work with
tabular reports:
■ Preview tables as you build them:
Preview your table as you select
variables and table options with a simple
drag-and-drop interface, and take
the guesswork out of table building
■ Control your table output: Choose from
a variety of formats to represent multiway information in a two-way table and
generate the view you want
■ Customize your table structure:
Exclude specific categories, display
missing value cells, and add subtotals
to your table
■ Get in-depth analyses: Run Chi-square,
column proportions, and column means
tests, and add more insight to your
tables to identify differences, changes,
or trends in your data
Automate frequent reports: Run large
production jobs and complex table
structures with ease to automatically
build similar tables with new data
Build tables interactively
Generate the table you envision using
the Graphical User Interface (GUI). Easily
work with output and present survey
results by using nesting, stacking, and
multiple response categories. Handle
missing values and change labels and
formats—even include missing values in
your results.
atthew Liao-Troth, assistant
professor of management, has to
juggle a heavy research workload with
teaching. But Liao-Troth has found that he
can maximize his research time and cover
more ground in the classroom by using
SPSS for Windows and the SPSS Tables
module. Liao-Troth describes his
experience working with SPSS Tables.
“ I really noticed a difference when I added the SPSS Tables
module to SPSS for Windows. It is so easy to use—what
used to take hours, now takes me less than five minutes.
SPSS Tables has not only helped with my regular research,
but it’s made a big difference in how I teach.
“For example, last quarter I was demonstrating the gender
effects on salary negotiations in one of my core business
classes. Thanks to SPSS and the SPSS Tables module, in a
one-and-a-half-hour class I was able to run the simulation,
enter the data, put it into tables, create graphs, and run the
statistical tests. Instead of describing to students what the
tables might look like hypothetically, we were able to discuss
the actual outcomes and concentrate on what we could learn
from the statistical tests.”
– Matthew Liao-Troth, Assistant Professor of Management,
Western Washington University
SPSS Tables makes report creation as easy as 1–2–3!
Preview tables as you build them with SPSS Tables’ drag-and-drop capabilities and a preview pane
Display information the way you want with SPSS Tables’ category management features
Give your readers reports that let them dig into the information—and make more informed
decisions—with SPSS Tables’ inferential statistics
Share results more easily with interactive pivot tables—quickly export to Word or Excel
Save time and effort by automating frequent reports using SPSS Tables’ syntax and automation
in production mode
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For more details and complete specifications, go to
Create presentation-quality tables from SPSS data—in a snap
Work with SPSS data
seamlessly and easily
Customize your table to show
the information you need
Create your table and easily
export your results
Once all your variables
are in place, push the
“OK” button to create
your final table. Apply
the optional TableLooks™
for a more polished
Add summary statistics, inferential stats, and subtotals to
make it easier to understand results.
Export your final
output to Word or
Excel. Your formatting
remains intact and
you can insert any
additional content to
describe your tables.
This example shows
a final report in Word.
Start with your SPSS data. SPSS Tables works seamlessly in
the SPSS environment so you can easily turn your data into
presentation-quality tables in no time.
Drag and drop your variables on the table preview builder
and see what your table looks like as you create it.
Product specifications
Graphical user interface
■ Simple drag-and-drop table builder
interface allows you to preview
tables as you select variables
and options
■ Single, unified table builder instead
of multiple menu choices and
dialog boxes for different table types
Control contents
■ Create tables with up to three
display dimensions: Rows (stub),
columns (banner), and layers
■ Nest variables to any level in
all dimensions
■ Crosstabulate multiple independent variables in the same table
Add additional variables by dragging and placing them
where you want.
Display frequencies for multiple
variables side-by-side with tables
of frequencies
■ Display all categories when
multiple variables are included
in a table even if a variable has
a category without responses
■ Display multiple statistics in rows,
columns, or layers
■ Place totals in any row, column,
or layer
■ Create subtotals for subsets of
categories of a categorical variable
■ Custom control over category
display order and ability to
selectively show or hide categories
■ Select from over 40 summary
For more details and complete specifications, go to
Calculate statistics for each cell,
subgroup, or table
■ Calculate percentages at any or all
levels for nested variables
■ Calculate counts and percentages
for multiple-response variables
based on the number of responses
or the number of cases
■ Select percentage bases for
missing values to include or
exclude missing responses
Formatting controls
■ Sort categories by any summary
statistic in the table
■ Hide the categories that make up
subtotals—remove a category
from the table without removing it
from the subtotal calculation
■ Directly edit any table element,
including formatting and labels
Sort tables by cell contents
in ascending or descending order
Automatically display labels
instead of coded values
Specify minimum and maximum
width of table columns (overrides
Show a name, label, or both for
each table variable
Display missing data as
blank, zero, “.”, or any other
user-defined term
Add titles and captions
Output as SPSS pivot tables
Specify corner labels
Customize labels for statistics
Display the entire label for
variables, values, and statistics
Choose from a variety of
numerical formats
Apply pre-formatted TableLooks
Define the set of variables that
are related to multiple response
data and save it with your data
definition for subsequent analysis
Accepts both long- and short-string
elementary variables
Imposes no limit on the number
of sets that can be defined or
the number of variables that can
exist in a set
Tests of significance:
– Chi-square
– Column means
– Column proportions
Exclude categories from
significance tests
Significance tests for multiple
response variables
Syntax and printing formats
■ Simpler, easy-to-understand syntax
■ Syntax converter (for upgrade users)
■ Specify page layout: Top, bottom,
left and right margins, and page
■ Use the global break command to
produce a table for each value of a
variable when the variable is used
in a series of tables
System requirements
■ SPSS 15.0 for Windows
■ Other system requirements
vary according to platform
1.800.253.2575 (U.S. only) Order now!
With SPSS Trends 15.0, you can:
SPSS Trends 15.0
Develop reliable forecasts quickly, regardless of the
size of the dataset or number of variables
In-depth support for time-series analysis
Find the best model for your data using the new Expert Modeler
Apply saved models to “what-if” scenarios to optimize your decisions
Update and manage forecasting models efficiently
Reduce forecasting error by automating appropriate
model selection and parameters
Gain more control over choices affecting models,
parameters, and output
Build expert time-series forecasts—
in a flash
Time-series analysis is the most powerful
procedure you can use to analyze historical
information, build models, predict trends, and
forecast future events. SPSS Trends 15.0 is the
best way to quickly create powerful forecasts
with confidence. With better forecasts, longterm goals can be set—with insight on how to
achieve them—based on your organization’s
past performance and knowledge of your
Unlike spreadsheet programs, SPSS Trends
has the advanced statistical techniques
you need in order to work with time-series
data. But you don’t need to be an expert
statistician to use it. Regardless of your level
of experience, you can analyze historical
data and predict trends faster, and deliver
information in ways that your organization’s
decision makers can understand and use.
Product specifications
Model a set of time-series
variables by using the Expert
Modeler or specifying the
structure of an ARIMA or
exponential smoothing model
■ Allow Expert Modeler to select
the best fitting predictor variables
and models
– Limit search space to only
ARIMA or only exponential
smoothing models
– Treat independent variables
as events
Specify custom ARIMA models
– Produces maximum likelihood
estimates for seasonal and
non-seasonal univariate models
– General or constrained models
specified by auto-regressive
or moving average order,
order of differencing, seasonal
autoregressive, or moving
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Deliver high-resolution graphs and communicate
results effectively
SPSS Trends 15.0 will help you find answers to
tough questions:
■ If I increase my advertising budget, how will
it affect sales by product or region?
■ How will increasing assembly line capacity
affect production?
■ Will a change in fees affect the number of
new customers we gain?
■ How will tuition increases affect enrollment?
If you’re experienced at forecasting, SPSS
Trends allows you to:
■ Control every parameter when building your
data model
■ Or use SPSS Trends’ Expert Modeler
recommendations as a starting point or to
check your work
If you’re new to building models from timeseries data, SPSS Trends helps you by:
■ Generating reliable models, even if you’re
not sure how to choose exponential
smoothing parameters or ARIMA orders, or
how to achieve stationarity
■ Automatically testing your data for
seasonality, intermittency, and missing
values, and selecting appropriate models
average order, and seasonal
– Two dependent variable
transformations: Square root
and natural log
– Automatically detect or specify
outliers: Additive, level shift,
innovational, transient,
seasonal additive, local trend,
and additive patch
– Specify seasonal and nonseasonal numerator, denominator,
and difference transfer function
orders and transformations
for each independent variable
Specify custom exponential
smoothing models
– Four non-seasonal model
types: Simple, Holt’s linear
trend, Brown’s linear trend,
and damped trend
– Three seasonal model types:
Simple seasonal, Winters’ additive, and Winters’ multiplicative
– Two dependent variable
transformations: Square root
and natural log
Detecting outliers and preventing them from
influencing parameter estimates
Generating graphs showing confidence
intervals and the model’s goodness of fit
Display forecasts, fit measures,
Ljung-Box statistic, parameter
estimates, and outliers by model
Generate tables and plots
to compare statistics across
all models
Eight goodness of fit measures
available: stationary R2, R2,
root mean square error, mean
absolute percentage error, mean
absolute error, maximum absolute
percentage error, maximum absolute error, and normalized BIC
Tables and plots of residual
autocorrelation function (ACF)
and partial autocorrelation
function (PACF)
Plot observed values, forecasts,
fit values, confidence intervals for
forecasts, and confidence intervals
for fit values for each series
For more details and complete specifications, go to
SPSS Trends 15.0 provides tremendous flexibility in creating forecasts with Expert Modeler.
Expert Modeler: This feature was specifically
designed to support forecasting with time-series
data. Now you can produce time-series models
even if you have little or no experience with timeseries data.
This screenshot of the time-series modeler shows how
it provides you with the ability to model multiple series
simultaneously. Because the module presents results in
an organized fashion, you can concentrate on the models
that need closer examination.
The Expert Modeler feature enables you to:
■ Automatically determine the best-fitting ARIMA
(autoregressive integrated moving average
process) or exponential smoothing model for
your time-series data
■ Model hundreds of different time series at once,
rather than having to run the procedure for one
variable at a time
■ Test your data for seasonality, intermittency,
missing values, and outliers
Also in SPSS Trends 15.0:
■ Save models to an XML file so that forecasts can
be updated without having to reset parameters
or re-estimate the model
■ Write scripts so that updates can be performed
This screenshot displaying a forecast for women’s apparel shows
how you can automatically determine which model best fits your
time-series and independent variables.
Filter output to fixed number
or percentage of best or worst
fitting models
Save predicted values, lower
confidence limits, upper
confidence limits, and noise
residuals for each series back
to the data set
Specify forecast period,
treatment of user-missing values,
and confidence intervals
Export models to an XML file
for later use by TSAPPLY
Apply saved models to new or
updated data
■ Simultaneously apply models
from multiple XML files created
■ Re-estimate model parameters
and goodness of fit measures from
the data or load from the saved
model file
■ Selectively choose saved models
to apply
For more details and complete specifications, go to
Override the periodicity
(seasonality) of the active dataset
Same output, fit measure,
statistics, and options
Export re-estimated models
to an XML file
Estimates multiplicative or
additive seasonal factors for
periodic time series
Multiplicative or additive model
Moving averages, ratios,
seasonal and seasonal
adjustment factors, seasonally
adjusted series, smoothed
trend-cycle components, and
irregular components
Decomposes a time series into
its harmonic components, a set
of regular periodic functions at
different wavelengths or periods
Produces/plots univariate or
bivariate periodogram and
spectral density estimate
Bivariate spectral analysis
Smooth periodogram values with
weighted moving averages
Spectral data windows available for
smoothing: Tukey-Hamming, Tukey,
Parzen, Bartlett, equal weight, no
smoothing, and user-specified
High-resolution charts available:
Periodogram, spectral and
cospectral density estimate,
squared coherency, quadrature
spectrum estimate, phase
spectrum, cross amplitude,
and gain
System requirements
SPSS 15.0 for Windows
■ Other system requirements
vary according to platform
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With SPSS Server you can:
SPSS Server 15.0
Analyze massive data files faster
Increase analysis speed and productivity
Use system resources efficiently
Maximize productivity with SPSS Server
In addition to analytical tools for the desktop, SPSS Inc. offers you an enterprise-level
solution—SPSS Server 15.0. With SPSS Server, you can help your organization deliver
enterprise-strength scalability and enhanced performance. You’ll benefit from added
speed, accessibility, scalability, and data centralization.
Increase productivity and facilitate
decision making
When you combine the strength of
SPSS Inc. world-class analytical tools and
techniques with the flexibility and speed
of server functionality, you have a powerful
solution for supporting better decision
making throughout your organization.
Also in SPSS Server 15.0
Reduce network traffic and improve performance with the data-free client feature
Score new data in the scoring engine
using previously created models via
the interface or syntax
Use Feature Selection and Naïve Bayes
analysis when working with large datasets
For more information about SPSS Server, visit
or call 1.800.253.2575 (U.S. only) and speak to an SPSS representative.
World’s largest mail-order wine company picks up the speed
irect Wines, the world’s largest mail order wine
company, is an experienced database user. However
over time it ultimately found that its existing systems
could not cope with the volume and number of mailings.
When searching the market, Jon White, Direct Wines
customer database analyst, said, “We were looking for
a huge leap in function and greater flexibility to do the
things we wanted to do without being dictated to by the
limitations of the system. We also wanted a system that
would be user friendly and not too statistical, so that
non-expert users could take advantage of it.”
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Direct Wines has a server running SPSS Server and three
workstations running the client version. SPSS is also used
as the data-retrieval system. White stated, “We could not
have grown as a company without SPSS.” He adds, “At
first sight, SPSS has an interface with many different
statistics options and transformation tools. However,
the speed of its transformation and manipulation is
staggering, and its ability to store syntax and scripts
allows analysis to be repeated many times with ease.”
Process data on the server, rather than on your client machine
Access data directly from the server and free up
desktop resources
Ensure security with secure sockets layer (SSL) protocol
and by requiring a login and password for server access
Increase the tools available for preparing data and creating
reports using features unique to the server version of SPSS
What’s new in
SPSS Server 15.0
Several new features and enhancements help your
enterprise increase its productivity.
Realize faster data preparation and analysis
■ Seamlessly utilize third-party multithreaded sorting
applications to speed up data preparation
■ Gain increased performance when temporary files
are striped over multiple disks based on the network
administrator’s settings. This provides much greater
speed in reading and writing large temporary files,
which are often associated with time-consuming tasks
such as sorting and aggregation.
Remove the need for multiple administration tools
■ If you’re a network administrator, use a single
Administrative Utility for working with SPSS Server,
Clementine Server, and SPSS Predictive Enterprise
Services™. With this utility, you can administer just
one, or any combination of these three products
installed at your site.
For more details and complete specifications, go to
Complete the Picture with SPSS Stand-alone Products
Amos ■ AnswerTree
■ Data Entry ■
SamplePower ■ Smart
Viewer Web Server ■
Clementine ■ Dimensions ■
Amos ■ AnswerTree ■ Data Entry
SamplePower ■ SmartViewer
Web Server ■ Clementine ■
Dimensions ■ Amos ■ Answer
Tree ■ Data Entry ■ Sample
Power ■ SmartViewer Web
Web Server ■ Clementine ■
Dimensions ■
“With SPSS products, I can focus on areas of analysis that have
been ignored in the past. We’re now actually using the data
we are collecting.”
It doesn’t matter what
industry you are in—it’s
crucial that you collect
all the necessary data and
analyze it to its fullest extent.
That way, you are sure to get the
most complete picture and not just a
snapshot of your data.
SPSS offers additional software tools that can be
used alone or along with SPSS for Windows. They
work seamlessly together to allow you to expand your
capabilities for a more complete analytical system.
Check out some of the predictive analytics
software that is at the cutting-edge of
information technology today on the
following pages.
For more details and complete specifications, go to
– Brian C. Maclachlan, Principal Adviser, Statistical Research and Analysis,
Queensland Fire and Rescue Authority
Here are a few of SPSS’ most
popular stand-alone products:
Amos 7.0 — Allows you to easily perform structural
equation modeling (SEM) to support your research
and theories by extending standard multivariate
analysis methods
AnswerTree 3.1 — Build better profiles, discover
segments, and target the right groups using powerful
decision-tree algorithms
SPSS Data Entry 4.0 — A complete system for
collecting and managing survey research data
SamplePower 2.0 — Save time, effort and money
by identifying the sample size you need
SmartViewer Web Server 5.0 — Deploy SPSS results
with interactive Web reporting
Clementine Desktop — Quickly add the power of
data mining to your analysis
mrInterview 3.5 and mrTables 3.5 — A complete
solution for survey analysis
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Amos 7.0
Easily perform structural equation modeling (SEM)
Quickly create models to verify how your variables affect each other
Move beyond regression to gain additional insight
Take your analysis to the next level
Whether you’re performing a program evaluation or developing attitudinal and behavioral
models, sometimes analysis using traditional statistical correlations just isn’t going to cut
it. Many researchers are turning to structural equation modeling (SEM) and Amos software
to go beyond traditional techniques to explore complex relationships and more terms in
their models.
Gain new insights by identifying important latent variables and conclusions
Amos builds models that more realistically reflect complex relationships because any
numeric variable, whether observed (such as non-experimental data from a survey) or
latent (such as satisfaction and loyalty) can be used to predict any other numeric variable.
You can gain additional insight into the causal nature and strength of the relationships
among variables.
Amos makes structural equation modeling easy
Amos’ rich, visual framework enables you to easily compare, confirm, and refine models.
Quickly build graphical models using Amos’ simple drag-and-drop drawing tools. Models
that used to take days to create are just minutes away from completion. And once the
model is finished, simply click your mouse and assess your model’s fit. Then make any
modifications and print a presentation-quality graphic of your final model.
Obtain Bayesian estimates of model parameters and other quantities
Bayesian analysis enables you to apply your subject-area expertise or business insight
to improve estimates by specifying an informative prior distribution. Markov chain
Monte Carlo (MCMC) is the underlying computational method for Bayesian estimation.
Why Amos is the first choice for SEM…
Easy-to-use interface: Just point, click, and drag, and
drop to build your model—no need to type commands
or write equations
Quickly find models that fit your data: Select one
yourself or use exploratory SEM to choose a model from
a large number of candidates
Easily create models in Amos: Test hypotheses and confirm
relationships among observed and latent variables
Discover unexpected relationships using path diagrams:
Gain valuable insight into the causal nature and strength of
the relationship among variables
What’s new in Amos 7.0
Amos 7.0 continues to expand your statistical options
based on Bayesian estimation. The MCMC algorithm is
faster and the MCMC tuning parameter can now be adjusted
automatically. You can now perform estimation with
ordered-categorical and censored data. This enables you to:
Create a model based on non-numerical data without
having to assign numerical scores to the data
Work with censored data without having to make
assumptions other than the assumption of normality
Impute numerical values for ordered-categorical and
censored data. The resulting dataset can be used as input
to programs that require complete numerical data.
Determine probable values for missing or partially
missing data values in a latent variable model
Impute missing values or latent variable scores
Choose from three data imputation methods: regression, stochastic regression, or
Bayesian. Use regression imputation to create a single completed dataset. Use stochastic
regression imputation or Bayesian imputation to create multiple imputed datasets. You
can impute missing values or latent variable scores.
Programming environment allows users to expand capabilities
Amos lets you specify models non-graphically by writing programs with Visual Basic or C#.
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For more details and complete specifications, go to
Amos makes structural equation modeling easy and accessible
Structural equation modeling’s approach to
multivariate analysis encompasses and extends
standard methods—including regression, factor
analysis, correlation, and analysis of variance.
Amos makes SEM easy. It’s the perfect modeling
tool for a variety of purposes, including:
Market research — Analyze customer satisfaction,
brand loyalty, or purchase behavior
Business planning — Create econometric
and financial models and analyze factors affecting
workplace job attainment
Academic program evaluation — Evaluate program
outcomes or behavioral models using SEM to
replace traditional stepwise regression
Select a data file
View output
Select the analysis
properties you wish
to examine, such as
standardized estimates
of parameters or
squared multiple
correlations. Constrain
parameters for
more precise models
by directly specifying
path coefficients.
Symbol indicates a new feature.
Modeling capabilities
■ Create structural equation models
(including such special cases as
path analysis and longitudinal
data models) with observed and
latent variables
■ Specify candidate models using
one of two methods:
– Specify each individual candidate model as a set of equality
constraints on model parameters
– Use SEM in an exploratory way.
Amos tries out many models
and suggests promising ones,
while using Akaike information
criterion (AIC) and Bayes information criterion (BIC) statistics to
compare models.
Fit confirmatory factor analysis
models, variance components
models, errors-in-variables models,
and general latent variable models
Analyze data from several populations at once
Save time by combining factor
and regression models into a
single model, and then fit them
Analyze multiple models simultaneously: Amos determines which
models are nested and automatically calculates test statistics
For more details and complete specifications, go to
Bayesian estimation
■ Fit models with ordered-categorical
and censored data
■ Markov chain Monte Carlo (MCMC)
■ Specify an informative prior distribution: Normal, uniform, or custom
Computationally intensive modeling
■ Evaluate parameter estimates with
normal or non-normal data using
powerful bootstrapping options.
The bootstrapping and Monte
Carlo capabilities in Amos make
it easy for you to obtain bias and
standard-error estimates for any
parameter, including standardized
coefficients and effect estimates.
■ Test multivariate normality and
perform outlier analysis
Specify your model
Use drag-and-drop drawing tools to quickly specify
your path diagram model. Click on objects in the
path diagram to edit values, such as variable names
and parameter values. Or, simply drag variable names
from the variable list to the object in the path diagram
to specify variables in your model.
Input data from a variety of file formats (SPSS, Excel, text files, or many others).
Select grouping variables and group values. Amos also accepts data in a matrix
format if you’ve computed a correlation or covariance matrix.
Select analysis properties
Product specifications
Amos output provides
standardized or unstandardized estimates
of covariances and
regression weights as
well as a variety of model
fit measures.
Hotlinks in the
help system link
to explanations
of the analysis in
plain English.
Assess your model’s fit
Make any modifications to your model and
print publication-quality output.
Analytical capabilities and statistical
■ Determine probable values for
missing or partially missing data
values in a latent variable model
■ Use full information maximum
likelihood estimation in missing
data situations for more efficient
and less biased estimates
■ Obtain an approximate confidence
interval for any model parameter
under any empirical distribution,
including standardized coefficients,
using fast bootstrap simulation
– Assess model fit with Bollen and
Stine’s bootstrap approach
– Calculate percentile intervals and
bias-corrected percentile intervals
■ Perform randomized permutation
tests to show whether equivalent or
better-fitting models can be found
Estimate means for exogenous
Recalculate the degrees of freedom
after adding new elements
or changing model constraints
Use a variety of estimation
methods, including maximum
likelihood, unweighted least
squares, generalized least
squares, Browne’s asymptotically
distribution-free criterion, and
scale-free least squares
Evaluate models using more than
two dozen fit statistics, including Chi-square; AIC; Bayes and
Bozdogan information criteria;
Browne-Cudeck (BCC); ECVI,
RMSEA, and PCLOSE criteria; root
mean square residual; Hoelter’s
critical n; and Bentler-Bonett and
Tucker-Lewis indices
Data imputation
■ Impute numerical values for orderedcategorical and censored data
■ Impute missing values and latent
variable scores
■ Choose from three different
methods: Regression, stochastic
regression, and Bayesian
■ Single or multiple imputation
System requirements
■ Operating system: Microsoft
Windows XP or 2000
■ Memory: 256MB RAM minimum
■ Minimum free drive space: 125MB
■ Web browser: Internet Explorer 6
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AnswerTree 3.1
High marks awarded to AnswerTree
Profile customer, citizen, or student groups easily
he Lafourche Parish School Board in Thibodaux, Louisiana, wanted to
measure progress by improving test scores, then use those test results to
enhance learning for students in their region. Chris Bowman, Technology
Manager of Lafourche Parish School Board, was commissioned to assist the
local board with this reform initiative.
Effectively target the right groups of people for
your sales and marketing campaigns
Choose from four decision-tree algorithms
Target the right people more effectively
using intuitive decision trees
Whether you have a limited statistical background or you’re a statistician, AnswerTree will
work for you. AnswerTree creates decision trees so you can easily identify the groups that
matter most and understand the attributes that influence their responses. AnswerTree
eliminates the costly guesswork that leads you to send offers to arbitrary people on your
mailing list. Take your analysis and modeling to a new level with these features:
Works with your existing system:
AnswerTree can stand-alone or can
launch from within SPSS. It reads SPSS
files and ODBC-compliant sources.
Highly visual decision trees: Spot
segments and patterns in your data
quickly. Easily expand and collapse
branches; zoom to nodes of interest;
and use summary statistics, gain
charts, and evaluation graphs to reach
conclusions quickly.
Interactive tree growth: When
making adjustments to a tree, the
model is affected and the tree is
redrawn immediately
Four algorithms: Find the best fit for
your data. Select from CHAID, Exhaustive
CHAID, Classification & Regression Trees
(C&RT), and QUEST
Model evaluation graphs: Use lift,
gains, and other evaluation graphs to
spot at which point the model offers
optimal results
Scalable algorithms: As data get larger,
you can solve enterprise-sized problems
more efficiently
Analyze your data with AnswerTree to:
Develop better-targeted direct mail programs
Offer specific products to the customer groups
most likely to purchase them
Create campaigns that increase customer retention
Product specifications
Key features
■ Display tree diagram, tree map,
bar graphs, and data
■ Build trees easily with a wizard
that prompts you through
model-building steps
Choose from three tree-generating
methods: Automatic, interactive, or
production mode
View nodes using one of several
ways: Show bar charts of your
target variables, tables, or both
in each node
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Improve credit scoring
Improve student retention and graduation rates
Develop more effective government programs
and services
Collapse and expand branches
without deleting the model
View and print trees horizontally
or vertically
Print large trees more easily using
the print preview
Re-run tree building using
production mode; generate scripts
Bowman needed a powerful software tool to perform sophisticated analyses of
test scores for more than 15,000 students. “We wanted to make decisions based
on an analytical appraisal of data,” explains Bowman. “SPSS for Windows and
AnswerTree put us in a position to get information that we never had before
using student scores from an annual assessment test.”
The tests are given each spring and the board typically has only a few
weeks to analyze and digest the data before they plan summer school
programs. “AnswerTree can quickly point out which factors influenced the
target variables so that we can be as productive as possible in a short time,”
says Bowman.
With the advanced analysis techniques in AnswerTree, Bowman dissected the
data in new ways—finding interesting, previously undiscovered relationships
among scores in various sections of the test. With the modeling power
of AnswerTree, Bowman highlighted the pertinent issues so that everyone—
from the school board to the principals to the teachers—could interact with
the results.
“Ultimately, it gave us a way to take our ever-shrinking dollars and put them
where they will do a lot of good,” he explains. “For the first time, principals
and teachers had a distinct set of rules to apply toward student achievement.
I would never have been able to do such sophisticated analysis without
Because of the revelations he uncovered, teachers had the time to develop
lesson plans to strengthen specific student skills, which should improve the
students’ overall scores.
automatically from the user
interface or edit models directly
from the script
■ Four powerful decision
tree algorithms:
– CHAID by Kass (1980)
– Exhaustive CHAID by
Biggs, de Ville and Suen (1991)
– Classification & Regression Trees
(C&RT) by Breiman, Friedman,
Olshen and Stone (1984)
– QUEST by Loh and Shih (1997)
■ Interactive evaluation graphs
enable visual representation
of gains summary table: Gains,
response, lift (index), profit and ROI
Misclassification chart: Describes
model performance, accuracy
versus actual and risk estimates
Gains chart: Identify segments by
highest (and lowest) contribution
and select nodes using this criteria
For more details and complete specifications, go to
Take your analysis and modeling to a new level
AnswerTree takes the output from
SPSS (and database files) and with a
few mouse clicks and a wizard-based
interface, creates decision trees.
Save time with easy
data access
Build better models,
faster, with many options
Easily spot important
segments and factors
Put the model to work for you
AnswerTree’s easy-to-use interface
allows you to adjust your model to
your specific situation. Get reliable
results quickly by training your model
on a subset of your data and then
testing the reliability on the remaining
portion. Use your models to batch
score other data for your desired
segments and focus your efforts on
the best targets.
Act on results quickly
Find the ideal cut-off point for
your marketing campaign using
AnswerTree’s evaluation graphs
(including gains, response, lift,
profit, and ROI charts). These
charts provide value summaries
of each segment and a clear picture
of your results so you can act on
your analysis quickly.
Quickly access data from popular database
sources using AnswerTree’s ODBC Wizard.
You can also import data directly as SPSS
Excel and ASCII files.
Fine-tune models
AnswerTree gives you three ways to build
models, including an intuitive Setup Wizard.
AnswerTree’s tree diagrams, tables,
and graphs are easy to interpret. Use
the trees to discover relationships that
are currently hidden within your data.
Make confident decisions
using sound results
Put your results to work
Instill confidence in your analysis using AnswerTree’s Gain
Summary, or its Misclassification chart, which provide a clear
report of your model’s accuracy and estimated error.
Enhance your model by training it on a subset
of your data, then testing its reliability on the
remaining portion.
Summary report: Document
analysis results as well as the
criteria used to build trees
■ Export:
– Trees as Windows bitmap (BMP)
or meta files
– Gains charts and risk summary
tables as tab-delimited text files
– Rules and summaries as text files
– Trees, gains charts and risk, rule
and summary tables as HTML
Export decision rules that define
selected segments in SQL to score
databases or SPSS syntax to score
SPSS files
Export XML models to score
cases using models developed in
AnswerTree or other systems
Import data from SPSS, Excel, and
text (ASCII) files
Get native access to database
management systems including
Oracle, SQL Server, DB2; additional
For more details and complete specifications, go to
AnswerTree results are reusable and easy to work
with. Simply save them as SPSS syntax or SQL,
and conveniently use them to score your database or extract records.
access to any ODBC-compliant
sources using the ODBC Wizard
System requirements
■ Operating system: Windows 98,
2000 or Windows NT 4.0 with
Service Pack 5 or higher
■ Hardware: Pentium or Pentium
class processor, SVGA monitor, and
CD-ROM drive for installation
Minimum free drive space: 70MB
for software
Minimum RAM: 64MB
Microsoft Internet Explorer 5.0 or
later for reading help documents
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The how’s and why’s of survey research:
getting the most value through an
effective survey research process
SPSS Data Entry 4.0
Quickly design paper-based surveys and desktop data entry forms
Eliminate the need to clean and prepare data for analysis
Directly compatible with SPSS analytical software
Your complete system for collecting
and managing survey research data
SPSS Data Entry products work together to provide an integrated system
for collecting and managing survey research data. Reap the rewards of
flexible, professional-looking survey design, and efficient on-screen entry
forms. Plus gain powerful data collection and cleaning capabilities—and
have data that’s ready for immediate analysis in SPSS. All SPSS Data Entry
products offer complete integration with SPSS for Windows, so you can
move from data collection to analysis in a single step.
Enhance your survey research process with SPSS Data Entry
Spell check feature:
Enable survey designers to check for spelling errors in design mode, eliminating the need to
redo work after errors are discovered in published surveys
64-byte variable names:
Create variable names of up to 64 bytes for more specific, descriptive names
4,000-character open response:
Enable almost any size text response—up to 4,000 bytes for complete, detailed information
Question Library:
Choose sample questions with responses and variable definition, and add new questions
SPSS (* .SAV) data file:
Automatically create SPSS .sav files (with defined dictionaries) as you build your survey forms
Product specifications
Status bar
Auto-backup, auto-save
Form creation
■ Drag-and-drop form design
■ Toolbox with many response
– Text box for long open responses
– Option button to select one from
Context-sensitive help, how-to’s,
and tips
Online tutorials
Open multiple surveys or forms
at once
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a list of choices
– Check box to select all that apply
(multiple response)
– List boxes to easily present a list
of options
– Drop-down lists to save space
– Combo boxes give space for customized responses (e.g., “Other”)
– Matrix to organize similar-style
questions in a grid
Define data file and SPSS
dictionary as you build the form
– Define value labels from
response text
– Define variable labels from
question text
(Excerpt from Step 1: Planning and survey design)
Write the questions
The key to a successful survey is to ensure that your questions
are concise and easy-to-understand. This way, you will get valid
and reliable information. No matter how well other features of the
survey are designed and executed, poor questions will reduce the
value of the data gathered.
Use well-written and tested pre-existing questions as much as
possible, especially from surveys done in your specific industry or
topic area. You can find well-written questions in question libraries.
Some software programs have question libraries built in, which
can help guide you through professionally written questions. Keep
in mind that no question is usable in every situation—so you have
to examine the questions for your particular survey research.
Pretesting questions is the best method to determine whether a
question is correct for your own survey. If you are going to write
questions on your own, you might consider taking a training course
to learn proven methods for question writing.
Design the questionnaire
A poorly formatted survey can deter people from responding
to your survey. It can also give skewed results. There are two
key goals to keep in mind when designing a questionnaire:
minimizing measurement error and reducing non-response.
Your questionnaire should be constructed so that:
■ Respondents are motivated to complete it
■ The questions are all read correctly and thoroughly
■ Respondents understand how to respond to each question or
how to skip, with clear instructions throughout the document
■ Returning the questionnaire is an easy and straightforward task
– Set missing values
– Define multiple response sets
– Enable definition of variable
– Long variable names permitted
Copy and paste variable properties
Drag-and-drop variables to
automatically create questions
Question Library of over 300
sample questions and responses
Automatic question renumbering
Spell-checker to catch errors
Customizable design with images
Annotation text for headings,
instructions, or comments
Flexible formatting capabilities
For more details and complete specifications, go to
SPSS Data Entry in the survey research process
Make data collection easier than ever before. Here are several capabilities that increase the convenience and
effectiveness of your survey research process…
Plan and write your survey in no time
Define your variables accurately
After you design your survey 4 define the data rules 5 and SPSS Data Entry Builder ensures
you collect accurate data 6 .
Begin with speedy form design 1 using preformatted questions 2 from Data Entry Builder’s
Question Library—choose from more than 300 questions. Or create your own. 3 Enable openended responses up to 4,000 characters. Plus, spell check is available in design mode.
Collect your data in less time
Analyze your data
With Data Entry Station, collect cleaner,
more accurate data in less time—data
entry staff and respondents are guided
through relevant questions.
Property Inspector to control
the look of every element of your
form, including colors, fonts,
size, and borders
Efficient entry screens
Produce printed surveys
Multiple form support in one file
Data collection
■ Skip-and-fill rules guide entry
■ Smart navigation features
■ Auto-jump automatically skips to
the next field after the maximum
number of characters is entered
■ Go to next case, go to case
■ Find and replace
Data entry verification
■ Powerful data cleaning rules
– Validation rules, checking rules,
and skip-and-fill rules
■ Flexible cleaning methods
■ Interactive rules checking
■ Batch mode rules checking
■ Compare versions verification
For more details and complete specifications, go to
Once your data is collected, it’s immediately
ready for analysis using SPSS.
Checking report by case or rules
Rules Wizard for step-by-step
rules setup
Rules editor with scripting for
advanced and customized rules
Copy rules between surveys
Standard or custom alerts
System requirements
(SPSS Data Entry Builder only)
■ Pentium® processor or higher
■ Microsoft Windows 98, 2000, Me,
or Windows NT 4.0
■ 16MB RAM; more for large surveys
■ 75MB hard drive space
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Political campaigns depend on
accurate sample sizes—do you?
SamplePower 2.0
“I cannot believe the
time SamplePower
saves and how easy
it is to use.”
– Mandel Bellmore,
Ph.D., President, Block,
McGibony, Bellmore
& Assoc., Health and
Hospital Consultants
Determine sample size with ease
Test results before beginning
Explore and tailor research parameters
campaign manager wishes to conduct a tracking poll.
With a $50,000 budget, she can conduct interviews at an
average cost of $50 per interview. What sample size should she
use in order to have adequate statistical power, get a statistically
significant result, and stay within her budget?
Save time, effort, and money by
identifying the sample size you need
SamplePower easily arrives at the sample size required. The
campaign manager selects a test that will give her a 10-point
spread. She sets up her parameters and explores the relationship
of power and sample size given the other specifications. Running
SamplePower, she discovers that an overall sample size of
approximately 800 will give her the desired statistical power.
SamplePower is the front end of a complete line of research and analysis
software from SPSS. Whether you’re an advanced or beginning statistician or
researcher, you’ll easily identify the appropriate sample size—every time—for
any research criteria. Strike the right balance among confidence level, statistical
power, effect size, and sample size using SamplePower.
SamplePower uncovered:
■ The poll costs only $40,000, so she saves 20% of her budget
■ She saves the time it would take to conduct the extra
200 interviews
■ She saves the effort it would take to enter extra data
collected in the interviews
Four reasons why SamplePower is the #1 choice for researchers:
1. Save time, effort, and money:
It’s crucial to have an accurate sample
size to prevent:
A missed research finding, when your sample
size is too small
Wasted time and resources, when your sample
size is too large
2. Present your results clearly:
Deliver impressive reports with the Report tool
Product specifications
Create scenario text reports
Find N for any power
■ Show Cohen’s effect size
conventions for specific tests
Working with results
■ Pivot tables
■ New export and print options,
including export to Excel
■ Display multiple graphs to quickly
assess the impact of various factors
either alone or in conjunction
■ Graphs linked to pivot tables and
to reflect the structure of the tables
General features
■ Set Alpha level; 1 or 2 tailed tests;
number of decimals displayed; N of
cases for min, max, and increment;
computational formula (some exact
formulas implemented); data entry;
and study design options
■ Show power and precision
(depends on test) with varied
sample sizes, power only, or with
varied effect sizes and Alphas
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3. Simple to learn and use:
Interactive guides help you determine an
effective sample size
4. Covers the statistics you need:
Access powerful statistical techniques
such as means and their differences, ANOVA,
regression, and more
Export graphs as WMF, EMF, BMP,
and Word or PowerPoint
Statistical tests
■ One-sample and paired t tests
when mean equals zero or equals
a specified value
■ Precision
■ t test for two independent groups
with variance known and unknown
■ One-sample test that proportion =
0.50 or specific value
Without SamplePower, the campaign manager might have spent
her entire budget, created unnecessary work, and arrived at her
result later than she did.
2x2 for independent samples
2x2 for paired samples (McNemar)
■ Sign test
■ KxC for independent samples
■ One-sample tests that correlation =
zero or specific value
■ Two-sample test that correlations
are equal: Computational option for
power and Fisher Z transformation
■ Oneway Analysis of Variance and
Analysis of Covariance
Factorial Analysis of Variance and
Analysis of Covariance: Two factors
and three factors
■ Templates for study design
■ Error model
Logistic regression
■ One continuous predictor or two
continuous predictors
■ One categorical predictor with
two or more levels
Survival analysis
■ Accrual options: Subjects entered
prior to first study interval, subjects
entered during study at constant
rate, and accrual varies
■ Hazard rate options: Constant and
■ Attrition rate options: No attrition,
constant rate, and rate varies
Equivalence tests
■ Equivalence tests for means and for
System requirements
■ Pentium-class processor; Microsoft
Windows 95, 98, 2000, or Windows
NT 4.0
For more details and complete specifications, go to
Get precise results faster with flexible, efficient tools
SamplePower is packed
with features that make
finding accurate sample
sizes easy. Convenience is
built in at every level, from
the smooth user interface
to behind-the scenes
statistical computations
that give results in seconds.
You get the clear, precise
answers you need to move
forward with your research.
Perform analysis in minutes
Make informed decisions
at every step
Once you’ve found a sample size, the
most important thing is being able to share
your results with clients or colleagues.
SamplePower gives you three fast and easy
ways to share results.
1. Report tool communicates your
results clearly
SamplePower’s interactive summary panel gives
you concise summaries of power and precision
at any point, so you can see how each decision
affects your results.
SamplePower’s interactive guide leads you
smoothly through your analysis. The guide
explains terms and takes you through the
steps necessary to determine an effective
sample size.
Compare results before you begin your research
See how your research
criteria will affect power
Get accurate guidance
with Cohen’s effect sizes
SamplePower’s Tool menu provides Cohen’s
effect size conventions, which allow you to
determine effect sizes for particular tests by
simply clicking on an icon. Cohen’s effect size
provides users with a “rule-of-thumb” for
determining otherwise ambiguous “small,”
“medium,” and “large” effect sizes. Plug these
effect sizes into the main screen to see how
varying the effect size affects power or precision.
SamplePower’s tables and graphs allow you to easily
assess how different combinations of your research
parameters (such as proposed sample size, Alpha
levels, and duration) affect your statistical power.
Simply use the Report tool and a complete
report is displayed on the screen. You have the
flexibility to embed the report in a word processor
document and to create convincing proposals.
2. Support your results with
embedded tables
The Table tool gives you a
table showing power and
precision at select sample
sizes. Easily view how
sample size affects power
or precision. Plus, you can
embed this table in a word
processor for informative
The Stored Scenarios tool gives you optimum control over the flow
of your research. You can vary Alpha level, power, effect size, or sample
size in the main screen and store your results as you continue. This
illustration shows how the sample size varies as other settings, such
as Alpha, are changed.
Present your results clearly
3. Enhance the understanding of
your research with graphics
Find sample sizes
in one click
For more details and complete specifications, go to
SamplePower’s Find N tool finds
the sample size for the default
power setting in one click. You
also have the flexibility to choose
different power-size settings to
compare results.
The Graph tool generates graphs relating power
to sample size. Graphs provide a visual aid that
help determine how sample size affects power.
Even include charts in documents.
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You can be an
SPSS star...
and receive a free gift.
We need your story about how you use SPSS products
We’re looking for stories about how SPSS users put SPSS
software to work for themselves and their organizations.
Tell us how SPSS add-on modules (SPSS Regression, SPSS
Conjoint, SPSS Tables, etc.)—or other SPSS products like
Amos, SamplePower, or SmartViewer Web Server—have
helped you get through a tough assignment.
Submit your story of any length—even a few sentences will
do—and it might be published in an upcoming product guide
(with your permission, of course). If we use your story, you’ll
receive a FREE SPSS prediction orb. Offer ends December 29,
2006, and is limited to the first 250 qualified entries.
Go to to submit your SPSS story.
Here are a few “starters” to help you tell your story:
My organization benefits from using (SPSS product) because...
I doubt if I would have discovered __________ from my data if I hadn’t used (SPSS product)...
My colleagues were amazed when I was able to do __________ by using (SPSS product)...
Most people don’t know that by using (SPSS product), you can __________ ...
If someone took (SPSS product) away, it would mean...
My favorite tip or trick with (SPSS product) is...
(SPSS product) saves me time by...
If we use your story, not only will you
be an instant star, but you’ll also receive
a FREE SPSS prediction orb.
Maybe you owned a prediction “8-ball” during your childhood. The SPSS
Prediction Orb provides you with “statistically correct” answers–just
ask the orb a question, then flip the ball over to receive answers such as
“No correlation,” “Beware the outliers,” or even, “You got a raw score.”
University of Chicago Hospitals
increase patient satisfaction with
Web reporting
SmartViewer Web Server 5.0
Easily share and review analytical reports in a Web environment
Interact with reports to immediately get the answers you need
Make more informed, timely decisions
Deploy SPSS results with interactive
Web reporting
For more
information, or
to place an
order, please call
Start making better decisions by sharing
strategic information using SmartViewer
Web Server, the analytical content portal
for SPSS products.
Analysts publish reports from SPSS for
Windows or Mac to SmartViewer Web
Server’s secure database. No special
downloads or installations are required
for information consumers to view the
reports using a standard Web browser.
SmartViewer Web Server centrally stores
all of your organization’s analysis,
including pivot tables, graphs, charts,
OLAP reports, and HTML content and
output from third-party products such as
Microsoft Office.
Instant results with Web-based
analytical reports
SmartViewer Web Server eliminates
the frustrations of static, paper-based
reports because it empowers you to:
■ Organize your reports within a
convenient home page which includes
customizable categories that enable
you to find what you need right away
■ Eliminate the need for someone to
create new reports when you want
different views of the information.
SmartViewer Web Server’s interactive
tables enable you to pivot rows,
columns, or layers, and drill down to see
the results in more meaningful ways.
.S. News & World Report recognizes the University of
Chicago Hospitals & Health System (UCHHS) every year
as one of “the nation’s best hospitals.” UCHHS maintains this
title by realizing the need for measuring patients’ feedback and
making continuous improvements that address their concerns–
based on more than 45,000 patient visits and 2,500 admissions
each month.
To ensure patient satisfaction, Kim Carli, patient satisfaction
measurement project manager at UCHHS, conducts continuous
surveys on every facet of patients’ hospital stay, from the
courtesy and knowledge of hospital staff to bathroom cleanliness.
After analyzing responses, she produces reports that provide
hospital managers a way to measure their performance in areas
where they can improve the quality of a patient’s stay, such as
courtesy and wait time.
When Carli first joined UCHHS, writing, formatting, and
delivering these reports took up to three weeks per month.
Not only did this delay reports from reaching hospital
managers promptly, it also prevented her from devoting the
necessary time to improve patient satisfaction.
Carli wanted to reduce the frustration and time of processing
reports by switching to an electronic-based delivery system. With
SPSS Inc.’s SmartViewer Web Server, she discovered a solution
that allowed her to customize, automatically publish, and rapidly
distribute reports via the Web.
As a result, the UCHHS:
■ Reduced report processing time from three weeks to one week
and freed up resources to improve patient satisfaction
■ Quadrupled the output of reports from 10 to 40 per month
■ Promoted a shift to “paperless” reporting with the aim
of making it part of the hospital’s intranet strategy
First, the analyst publishes a report using SPSS. Recipients then access SmartViewer Web Server
to view and interact with the results in their Web browsers to view the information most relevant
to them.
For more details and complete specifications, go to
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Clementine Desktop
Quickly add the power of data mining to your analysis
Keep the convenience and cost-effectiveness of working from your desktop
Take advantage of an intuitive graphical interface to visualize every data mining step
Quickly add the power of data mining to your analysis
Data mining enables you to generate new
hypotheses from data. Users of SPSS statistical
tools have the algorithms, computer power,
and statistical expertise to dig deeply into a
large amount of data; many also look to data
mining for an even broader analysis. Data
mining uncovers previously unknown patterns
and connections in data, enabling you to
improve business processes and make the right
decisions at the right time.
SPSS offers Clementine Desktop to enable you
to realize the benefits of data mining for smaller
problems where the power of server software
and enterprise integration is not required.
Clementine Desktop can be installed quickly
on a personal computer—so you can begin
mining your data right away. These products are
designed with business users in mind, so you
don’t need to be an expert in data mining to
enjoy its benefits.
Clementine is SPSS’ industry-leading data
mining solution
With Clementine Desktop, your business
knowledge guides the process
Statistical analysis tests hypotheses, one at a
time. Data mining generates its own hypotheses
automatically—often many at one time. Like a
satellite deployed over your organization’s world
of complex data, Clementine uses techniques
including rule induction and neural networks
to reveal patterns that were previously hidden
in masses of data. You can combine the data
mining power of Clementine with the statistical
might of SPSS to gain new insights into some of
your organization’s toughest issues.
The product’s intuitive graphical interface makes
it easy to visualize every step of the data mining
process. You don’t need to write code; instead,
you can focus on knowledge discovery and
pursue “train-of-thought” analysis. This leads to
data mining productivity—a faster achievement
of your business goals and a quicker “timeto-value.” Clementine Desktop can also be
enhanced with a Text Mining module, so you can
extend your analysis to unstructured data.
Customer scenario: how Clementine and
SPSS work together for in-depth analysis
A cell phone service provider uses Clementine’s data mining
ability to discover which customers are most likely to switch to
another provider. The analysis identifies that males age 30-40
with a particular model handset are 50 percent likely to churn.
This is a totally new insight that contradicts the company’s
previous “conventional wisdom.”
To ensure that a Clementine model has truly identified something
new, company analysts often run a number of statistical analyses
using SPSS. They measure the statistical significance of the
discovered pattern and perform a “survival analysis” to help
understand the length of customer relationships (before they churn).
These procedures give the company additional confidence that
they are committing their resources wisely to address this churn
profile identified by Clementine. Finally, the company uses SPSS
to create charts and graphs that are placed in a Word document
report summary that provides recommendations to decision makers.
Clementine allows you to visualize the complete data mining process.
Clementine Desktop is a component of the larger Clementine family of products. To learn more about
how you can economically add data mining to your organization’s arsenal of data analysis, contact an
SPSS representative at 1.800.253.2575 (U.S. only). Or visit
1.800.253.2575 (U.S. only) Order now!
For more details and complete specifications, go to
mrInterview 3.5 and mrTables 3.5
The perfect survey solution for organizations
looking for:
A data collection platform that is fully integrated with SPSS for Windows
Software that can grow as needs progress
Secure, online access for users
A way to streamline their research processes
The ability to conduct research in any language
Centralize survey data collection and analysis
■ Create visually appealing surveys through Web, phone, paper, and handheld devices
■ Create tables that analysts can immediately interact with online
“As our primary data collection
engine for online interviewing,
Create and conduct surveys with confidence using mrInterview
mrInterview enables us to deploy
sophisticated surveys online. The
depth of mrInterview’s features,
including sample management
and quota control, coupled
with its customization options,
makes it an ideal addition to our
research infrastructure.”
– Michael Reuscher, Vice President,
IT project management, Synovate
mrInterview is a complete solution for
fielding and managing large or small Web
projects with maximum efficiency—all
without sacrificing the sophistication
that professional researchers rely on. It
includes the security, access, permissions,
and features you need to easily create and
administer surveys and report on data
through a Web browser.
You can adapt mrInterview to your
employees’ experience level—from
beginner to expert—enabling them to
create simple or complex surveys.
Whether your organization runs a few
surveys per year or has extensive data
collection operations, mrInterview
adapts to fit your needs.
Easy to use
Through mrInterview, you can ensure
security by assigning permissions for a
variety of users. Permission levels range
from full access for individuals, such as
your survey creators, to view-only access
With mrInterview, you can easily set up
and run projects, moving smoothly from
authoring to activation and testing, and
then to the analysis and exporting of data.
Secure deployment
Share survey research tables online with mrTables
Trends, opinions, and attitudes are constantly changing. To keep colleagues and clients up to date on the
most recent findings, you need an efficient way to share your results.
mrTables enables you to share your research tabulation results online with anyone, anywhere, via your
Web browser. Present results to clients in another department, office, city, or country. Create tables
that analysts can immediately interact with online. Use data in different formats—and even different
languages—without changing the user environment. Even publish your survey reports to Microsoft Excel
and other popular reporting applications.
For more details and complete specifications, go to
of results for your clients. Everyone gains
access through a common interface—and
you control who sees what, based on
permissions you set.
To keep your Web-accessible content
secure, you can keep your response data
on a separate machine, behind a firewall.
You can also easily add specialized, thirdparty security systems to mrInterview.
mrInterview and mrTables are
part of the SPSS Dimensions survey
research platform
All Dimensions products are designed to work with each
other for seamless survey research. The Dimensions line
enables organizations to control and optimize the entire process
of collecting, analyzing, and managing customer data.
For more information visit
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Using the REPLACE function with SPSS to clean up data sets
Clean up your datasets with the
REPLACE function
Figure 1: There are
several strings of
characters that need
to be replaced
The REPLACE function in SPSS 15.0 makes it easy to clean up
your dataset. Now you can replace one string character with
another. The REPLACE function allows you to remove spaces and
characters, replace one word with another—and much more.
Clean your dataset up in no time at all.
Example 1:
To remove the spaces between the values in variable “id” and remove the quote marks
under “responses,” replace these characters with null values. (Figures 1-2)
COMPUTE id = REPLACE(id,” “, “”).
Figure 2: You’ve
replaced the
spaces between the
variables under the
variable “id” and
removed the quote
marks around the
characters under
COMPUTE responses = REPLACE(responses,’”’,””).
To replace capital letters with lowercase letters: (Figure 3)
IF (INDEX(responses,’ABODES’))>0 responses=REPLACE(responses,’ABODES’,’abodes’).
Other examples include...
To replace a single letter:
IF (INDEX(responses,’Abodes’))>0 responses=REPLACE(responses,’A’,’a’).
IF (INDEX(responses,’abodeS’))>0 responses=REPLACE(responses,’S’,’s’).
Finally, you can also replace a word with another word:
IF (INDEX(responses,’abodes’)) > 0 house=REPLACE(responses,’abodes’,’homes’).
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Figure 3: The capital
letters in the third
“responses” line
(ABODES) have been
replaced with lowercase letters.
SPSS In the News
Noteworthy headlines and articles
Marc Ecko Makes Designs on More SPSS
Software for Brand Performance Analysis
arc Ecko Enterprises has purchased additional
SPSS predictive analytics technology to help
analyze brand performance at a retail level. As Marc
Ecko Enterprises has achieved significant success
using SPSS to analyze its various apparel brands at a
wholesale level, implementing additional SPSS software
will enable the global fashion and lifestyle company to
gain a new perspective on brand management.
The company, that reported international retail sales
of approximately $1.2 billion in 2005, purchased more
SPSS software so it could achieve a complete view of
key business metrics and customer trends. Worldwide,
16 of the top 20 retailers use SPSS software.
“SPSS has become critical to all aspects of our company,
from the design of our various lines to the way our sales
team goes to market,” said Mark Faber, senior vice
president of Business Solutions, Marc Ecko Enterprises.
“With SPSS, we’re able to identify key growth areas and
gain a competitive edge with our customers.”
Brand names under Marc Ecko Enterprises include
ecko unltd., the world famous rhino brand; Marc Ecko
“Cut & Sew,” a contemporary menswear line launched
Fall 2004; G-Unit® clothing and accessories, a joint
venture with multi-platinum musician, 50 Cent; Zoo
York®, a line of action sports-inspired clothing and
accessories; and Avirex, a recently acquired mid-tier
sportswear collection.
SPSS Software Helping to
“Mushroom” Business for
International Produce Company
Continued from page 5
“With this software, employees are spending a lot less time
waiting for reports and gathering information, and more time
analyzing the business and making the necessary changes to
meet their business goals,” said Burnham.
Monterey Mushrooms president and chief executive officer
Shah Kazemi is so pleased with SPSS software that he has
had it implemented for all senior-level personnel to help them
better understand their revenues and margins. SPSS software
lets executives view and interact with customized reports—
which are all based on the same information—to help them
make the right decisions quickly.
“Because of SPSS, we’re able to see our data as we’ve never
seen it before,” added Burnham. “We’re able to view it
from different angles and perspectives. That allows us to
better analyze our margin of profitability and highlight
exceptions quickly, which, in turn, allows us to make better
business decisions.”
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SPSS Directions
Nov. 5-9
North American User Conference
Th e F a i r m ont C h i c a go , C h ic a g o, Il lin ois
Join us at the SPSS Directions
North American User Conference and:
Attend fascinating and relevant keynote presentations
Enjoy educational break-out sessions
Expand your knowledge with pre- and post-conference training courses
Network with colleagues and SPSS experts
Preview new and emerging technologies
See software demonstrations at the SPSS Demo Center
Visit the SPSS Expo Center to see new trends in analytical software
Learn tips, tricks, and techniques to master your SPSS software
Tour SPSS world headquarters (limited availability–register early)
Keyno te Spea ker
Steven D. Levitt
– Bestselling Author, FREAKONOMICS
– FREAKONOMICS has been on The
New York Times Bestseller List for
over a year
Register today:
Printed in the U.S.A. © 2006 SPSS Inc. All rights reserved. SCATV10-0806