A Guide to SPSS Statistics

IBM SPSS Statistics: A Guide to Functionality
IBM SPSS Statistics is a renowned statistical analysis software package that encompasses a
broad range of easy-to-use, sophisticated analytical procedures. In fact IBM SPSS Statistics is
comprised of a number of optional add-on modules that address specific analytical
requirements. These optional modules may be seamlessly integrated with the core ‘Base’
product. Nevertheless, the IBM SPSS Base product itself provides an extensive array of
univariate and multivariate statistical analysis procedures, extensive file and data
management routines as well as numerous graphical and analysis output options. This
document should serve as a guide for those interested in which particular procedures and
functions may be accessed within IBM SPSS Base as well as the various IBM SPSS Statistics
modules and their associated bundles (Standard, Professional and Premium).
Figure 1. IBM SPSS Statistics Base & Associated Optional Modules

IBM SPSS Statistics Base – Analytical Procedures
As a general overview of the analytical functionality within IBM SPSS Base, the following
procedures all appear as menu items within the core product.
• Reports – This menu item contains procedures for creating a wide range of summary
and listing reports.
• Descriptive Statistics – This includes an extensive range of descriptive statistical
functions including frequency tables, crosstabs and the Explore procedure which
generates histograms, boxplots and confidence intervals.
• Compare Means - Generates Means comparison tables as well as parametric T-Tests
and one-way ANOVA.
• Correlate - Includes Bivariate Correlations (Pearson and Spearman) and Partial
Correlation procedures.
• Regression - includes the Automatic Linear Models procedure as well as Linear
Regression, Curve Estimation, Partial Least Squares and Ordinal regression.
• Classify - Includes three clustering methods (Two Step, K-Means, and Hierarchical) also
includes Discriminant function analysis.
• Data Reduction - Includes Principal Component Analysis and Factor Analysis.
• Scale - Contains functions to perform Reliability analysis and Multi-Dimensional Scaling
(ALSCAL).
• Non-Parametric Tests - Contains a range of Non-Parametric tests for one sample,
independent samples and related samples.
• Simulation – This procedure allows users to create data that can be applied to existing
models or entered manually as equations.
• Time Series - Provides a number of descriptive time series procedures including
sequence charts and auto correlations.
• Multiple Response - Allows users to define multiple response sets for frequency and
cross-tabulation analysis.
• Quality Control - Includes Control charts and Pareto charts.
The following section provides more detailed information relating to the analytical
procedures found within IBM SPSS Base.

Reports Menu
• Used for creating listing reports similar to those produced by database software
• Can list values for individual cases or summary statistics broken down by a grouping
variable
• OLAP Cubes are used to produce layered reports which users can ‘drill down’ into via
the Output Viewer
• Often used for financial reports
• Both Report Summaries in Columns and Report Summaries in Rows produce plain
text output rather than the normal SPSS formatted text output
Descriptive Statistics Menu
• Frequencies and Descriptives are used to summarise individual variables (categorical
and continuous respectively)
• The Explore procedure provides a large collection of statistical and graphical routines
for examining distributions including histograms, m-estimators, Box plots and Stem
and Leaf plots
• Crosstabs is one of the most widely used procedures to examine the relationship
between categorical variables. The procedure also contains a number of statistical
tests and association measures
• TURF (Total Reach and Frequency) analysis is a procedure used for providing
estimates of media or market potential and devising optimal communication and
placement strategies given limited resources. TURF analysis identifies the number of
users reached by a communication, and how often they are reached.
• Ratio provides a comprehensive list of summary statistics for describing the ratio
between two scale variables. Often used in analysing real estate data to investigate
the ratio of valuations to actual selling price
• PP and QQ plots are generally used to evaluate whether or not the distribution of a
variable matches a given distribution (e.g. normal, laplace, logistic, lognormal etc.)
Compare Means Menu
• The Means procedure provides reports of summary statistics (not just averages)
broken down by independent grouping variables. It is a flexible procedure that also
includes tests for linearity and eta values

• The One-Sample T-Test is a parametric procedure that tests whether the mean of a
single variable differs from a specified target value.
• The Independent-Samples T-Test is a classic statistical procedure to compare the
means for two groups of cases
• The Paired-Samples T-Test procedure compares the means of two variables for a
single group. Often used to examine differences in before and after situations
• The One-Way ANOVA procedure provides the ability to test for significant
differences between more than two groups (i.e. k groups). It also allows users to
identify where the actual differences are using an extensive range post-hoc tests
Correlate Menu
• The Bivariate Correlation procedure provides both parametric and non-parametric
measures of association between variables. Correlation coefficients are widely used
to identify the strength of the linear relationships between pairs of variables
• The Partial Correlations procedure computes partial correlation coefficients that
describe the linear relationship between two variables while controlling for the
effects of one or more additional variables
• Examples of this might include examining the relationship between crime rates and
average property value but controlling for unemployment levels. This would allow us
to test whether or not any apparent relationship between crime and property value
existed irrespective of the local unemployment rate.
• The Distance measure calculates a wide variety of statistics measuring either
similarities or dissimilarities (distances) either between pairs of variables or between
pairs of cases. It is often used as a supporting exploratory procedure for techniques
such as cluster or factor analysis
Regression Menu
• Automatic Linear Models is a procedure that enables automatic model selection and
data preparation for estimating the values of a continuous target field. The
procedure also allows users to invoke ‘bagging’ routines to create ensemble models
with an emphasis on maximising model stability or alternatively ‘boosting’ routines
to create ensemble models that are focussed on estimation accuracy.
• The Linear Regression procedure is the all-time classic predictive algorithm. This
procedure supports multiple linear regression with a number of stepwise procedures
and fit measures. Classic examples include building predictive models to estimate
sales based on advertising spend, discount values, competitor price etc.

• The Curve Estimation procedure produces estimation statistics and related plots for
11 different types of curve. This allows users to investigate the most appropriate
curvilinear fit between variables.
• Partial Least Squares is a predictive technique that is an alternative to, for example,
ordinary least squares (OLS) regression and it is particularly useful when predictor
variables are highly correlated. PLS combines features of principal components
analysis and multiple regression. Note however that PLS is an extension command
that requires the IBM SPSS Statistics - Integration Plug-in for Python to be installed
on the system where you plan to run PLS.
• The PLS Extension Module must be installed separately, and can be downloaded
from http://www.ibm.com/developerworks/spssdevcentral
Classify Menu
• Two-Step Cluster reveals natural groupings (or clusters) within a dataset that would
otherwise not be apparent. The Two-Step procedure allows the use of both
categorical and continuous fields, is highly scalable and can automatically select the
number of clusters.
• K-Means Cluster (otherwise known as Quick Cluster) is a relatively fast and simple
cluster technique that requires continuous (or dichotomous) data in order to
effectively group cases together. K-Means also requires that the number of clusters
to be created is specified beforehand.
• Hierarchical Cluster Analysis also attempts to group similar cases together in order to
create clusters. As with K-Means, the number of clusters must be specified
beforehand and the data should be continuous (or dichotomous). Hierarchical
Cluster is a resource-intensive procedure that requires a distance matrix to be
computed in the background showing the distance between every single. As such,
although it is possible to follow the clustering process in detail, this technique is
generally used with small datasets.
• Discriminant Analysis can be used to build a predictive model for different groups. It
uses linear combinations of continuous predictor fields in order to create
discriminant functions. Examples might include building a predictive model to
estimate which of three tariffs subscribers to a new mobile phone service will sign up
to based on their age, income level, previous history of phone usage and size of
family.

Dimension Reduction Menu
• The Dimension Reduction menu offers both Factor Analysis and Principal
Components Analysis (PCA) as tools to uncover the underlying factors that explain
correlations in a set of variables.
• Factor Analysis and PCA allow analysts to distil the factors that underpin a number of
correlated variables. Examples of Factor Analysis include examining the underlying
factors that drive customer satisfaction. In such an example, factor analysis could
identify the top four composite factors when respondents have been asked to rate
the performance of a company using 30 different rating scales.
• The factor analysis procedure offers a high degree of flexibility:
– Seven methods of factor extraction
– Five methods of rotation
– Three methods of computing factor scores
Scale Menu
• Reliability Analysis measures the consistency of a set of measurements or a given
‘measuring instrument’. This can take the form of assessing the degree to which
scales or instruments provide consistent results over time or whether two
independent assessors give similar scores (known as inter-rater reliability).
• Multidimensional Scaling (ALSCAL) attempts to find the structure in a set of distance
measures between objects or cases. This task is accomplished by assigning
observations to specific locations in a conceptual space (usually two or three-
dimensional) such that the distances between points in the space match the given
dissimilarities as closely as possible. In many cases, the dimensions of this conceptual
space can be interpreted and used to further understand your data.
• A wider range of scaling techniques is available in the IBM SPSS Categories module.
Non Parametric Tests Menu
• The Non-Parametric Tests menu contains a collection of tests designed for situations
where, for example, the test data does not sufficiently meet the requirements of
many classical statistical tests.
• Example situations include comparing values from two distributions that are not
normally distributed or which are based on rank order values (such as rating scales).

• The tests that are available in these dialogs can be grouped into three broad
categories based on how the data are organized:
– A one-sample test analyses one field.
– A test for related samples compares two or more fields for the same set of
cases.
– An independent-samples test analyses one field that is grouped by categories
of another field.
• The Non-Parametric Tests procedure also includes access to a number of legacy
dialogs that allow direct access to a host of popular non-parametric procedures
including:
– Mann-Whitney U (two independent samples)
– Kruskall-Wallace H ( more than two independent samples)
– Wilcoxon signed-rank and McNemar tests (two related samples)
– Friedman (more than two related samples)
Simulation Menu
• Simulation allows users to examine ‘what if’ scenarios by testing the effect of
changing the values of key inputs in a predictive model. For example, if you have a
response model that includes advertising spend as an input, you can use the
simulation procedure to model the effect of reducing or increasing the advertising
spend to determine how the response rate changes.
• Simulation in IBM SPSS Statistics uses the Monte Carlo method. Uncertain inputs are
modeled with probability distributions (such as the triangular distribution), and
simulated values for those inputs are generated by drawing from those distributions.
Inputs whose values are known are held fixed at the known values. The predictive
model is evaluated using a simulated value for each uncertain input and fixed values
for the known inputs to calculate the target (or targets) of the model. The process is
repeated many times (typically tens of thousands or hundreds of thousands of
times), resulting in a distribution of target values that can be used to answer
questions of a probabilistic nature.
• To run a simulation, you need to specify details such as the predictive model, the
probability distributions for the uncertain inputs, correlations between those inputs
and values for any fixed inputs. Once you've specified all of the details for a
simulation, you can run it and optionally save the specifications to a simulation
plan file. You can share the simulation plan with other users, who can then run the
simulation without needing to understand the details of how it was created.

Time Series Menu
• The Sequence Charts procedure provides simple, exploratory line charts for time-
based data. It allows different sequences of values to be illustrated and compared
and offers some basic transformation functions (natural log, difference and seasonal
difference)
• Auto Correlations is an exploratory procedure which returns auto correlation and
partial autocorrelation information in the form of statistics and charts including
transformation functions such as natural log, difference and seasonal difference.
• Auto Correlations are useful for finding repeating patterns in a sequence. They in
effect, measure the degree to which the preceding values correlate with subsequent
values and as such are useful in exploring Time Series data.
• Cross Correlations are used when measuring information between two different
time series. They return information that show how strongly related the sequences
are to each other across a timeframe. Cross Correlations are a useful exploratory
procedure in Time Series when analysts wish to identify sequences or series which
are good predictors of each other.
Multiple Response Menu
• The Multiple Response procedure is used to analyse data where the categories are
not mutually exclusive. Examples of this include asking respondents in a
questionnaire to ‘Please tick all that apply’ or when analysing where customers
made purchases from 40 different products groups.
• The Multiple Response procedure requires users to define a multiple response set:
that is, they must identify all the variables that record the non-mutually exclusive
selections and save them as a set.
• Users can then include the newly defined multiple response set in special multiple
response frequencies and crosstabs procedures which are found under the Multiple
Response menu.
Note that:
• Using this particular procedure means that Multiple Response sets cannot be saved
and must be redefined each time a new session is started.
• Furthermore, the Multiple Response procedure within SPSS Statistics Base is
independent of the Multiple Response procedure in the IBM SPSS Tables module
(where sets may be saved for re-use).

• In SPSS Statistics Base, the Multiple Response procedure does not contain any
statistical tests.
Quality Control
• The Quality Control menu contains two charting techniques: Control Charts and
Pareto Charts. Both of these chart types are often used in quality control
applications to monitor or investigate changes in quality and to identify the main
causes of change.
• Control charts are used to determine whether a manufacturing or business process is
in a state of statistical control or not. A control chart is a specific kind of graph that
monitors a process over time with a view to identifying significant change to the
process rather than just natural variability.
• Pareto charts are specific types of bar chart used in quality control situations. The
purpose of the chart is to highlight in descending order, the main causes of defects/
quality errors/ events where a process has moved beyond the allowed control limits.
The following figures illustrate which optional modules are provided with the Standard,
Professional and Premium editions of IBM SPSS Statistics respectively.
Figure 2. IBM SPSS Statistics Standard

Figure 3. IBM SPSS Statistics Professional
Figure 4. IBM SPSS Statistics Premium

A Guide to SPSS Statistics

More Related Content

What's hot (20)

Similar to A Guide to SPSS Statistics (20)

More from Luke Farrell (14)

Recently uploaded (20)

A Guide to SPSS Statistics