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PASW Statistics Base 18
(formerly SPSS Base (main program))
Analyze Data Using Comprehensive Statistical Software
Make better decisions with better analysis using PASW Statistics Base
 PASW Statistics Base forms the basis of
many deployments with statistical tests and procedures that are fundamental to many analyses.
You can take the analytical process from start to finish with PASW Statistics Base. In addition to the data preparation, data management, output management and charting features now available in all
PASW Statistics modules, PASW Statistics Base offers the most frequently used procedures for statistical analysis that are the foundation for many analyses.
The procedures within PASW Statistics Base will enable you to get a quick look at your data, formulate hypotheses for additional testing, and then carry
out a number of procedures to help clarify relationships between variables, create clusters, identify trends and make predictions.
In PASW Statistics Base 18, you'll find the following enhancements:
- New nonparametric tests allow multiple comparisons and operate on large datasets more efficiently. Tests include: Chi-square, binomial, runs, one-sample, two independent samples, k-independent samples,
two related samples, k-related samples and descriptives.
- Charts for statistical process control tests now include rule-checking on secondary charts
Features
Descriptive Statistics
- Crosstabulations - Counts, percentages, residuals, marginals, tests of independence, test of linear association, measure of linear association,
ordinal data measues, nominal by interval measures, measure of agreement, relative risk estimates for case control for and cohort studies.
- Frequencies - Counts, percentages, valid and cumulative percentages, central tendency, dispersion, distribution and percentile values.
- Descriptives - Central tendency dispersion, distribution and z scores.
- Descriptive ratio statistics - Coefficient of dispersion, coefficient of variation, price-related differential, and average absolute deviance.
- Compare means - Choose whether to use harmonic or geometric means; test linearity; compare via independent sample statistics; paired sample statistics or one sample t test.
- ANOVA and ANCOVA - Conduct contrast, range and post hoc tests; analyzed fixed-effects and random-effects measures; group descriptive statistics; choose your model based on four types of the sum-of
-squares procedure; perform lack-of-fit tests, choose balanced or unbalanced design; and analyze covariance with up to 10 methods.
- Correlation - Test for bivariate or partial correlation, or for distances indicating similarity or dissimilarity between measures.
- Nonparametric tests - Chi-square, Binomial, Runs, one sample, two independent samples, k-independent samples, two related samples, k-related samples.
- Explore - Confidence intervals or means; M-estimators; identification of outliers; plotting of findings.
Tests to Predict Numerical Outcomes and Identify Groups
- Factor Analysis - Used to identify the underlying variables, or factors, that explain the pattern of correlations within a set of observed variables.
In PASW Statistics Base, the factor analysis procedure provides a high degree of flexibility, offering: Seven methods of factor extraction. Five methods of rotation, including direct oblimin and promax for
nonorthogonal rotations. Three methods of computing factor scores. Also scores can be saved as variables for further analysis.
- K-means Cluster Analysis - Used to identify relatively homogeneous groups of cases based on selected characteristics, using an algorithm that can handle large numbers of cases but which requires you to
specify the number of clusters.
- Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only.
- Optionally, specify variable whose values are used to label casewise output and request analysis of variance F statistics.
- Hierarchical Cluster Analysis - Used to identify relatively homogeneous groups of cases (or variables) based on selected characteristics using an algorithm that starts with each case in a
separate cluster and combines clusters until only one is left. Analyze raw variables or choose from a variety of standardizing transformations. Distance or similarity measures are generated by the Proximities
procedure. Statistics are displayed at each stage to help you select the best solution.
- TwoStep Cluster Analysis - Group observations into clusters based on nearness criterion, with either categorical or continuous level data; specify the number of clusters or let the number be chosen
automatically.
- Discriminant - Offers a choice of variable selection methods; statistics at each step and in a final summary; output is displayed at each step and/or in final form.
- Linear Regression - Choose from six methods: backwards elimination, forced entry, forced removal, forward entry, forward stepsize selection,
and R2 change/test of significance; produces numerous descriptive and equation statistics.
- Ordinal Regression—PLUM - Choose from seven options to control the iterative algorithm used for estimation, to specify numerical tolerance for checking singularity, and to customize output; five link
functions can be used to specify the model.
- Nearest Neighbor Analysis - Use for prediction (with a specified outcome) or for classification (with no outcome specified); specify the
distance metric used to measure the similarity of cases; and control whether missing values or categorical variables are treated as valid values.
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