Analyze raw variables or choose from a variety of standardizing transformations. 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.Select one of two methods for classifying cases, either updating cluster centers iteratively or classifying only. 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.Also, scores can be saved as variables for further analysis Three methods of computing factor scores.Five methods of rotation, including direct oblimin and promax for nonorthogonal rotations.In IBM SPSS Statistics Base, the factor analysis procedure provides a high degree of flexibility, offering: Factor Analysis – Used to identify the underlying variables, or factors, that explain the pattern of correlations within a set of observed variables.You can be confident that you’ll always have the analytic tools you need to get the job done quickly and effectively. IBM SPSS Statistics Base contains procedures for the projects you are working on now and any new ones to come. Tests to Predict Numerical Outcomes and Identify Groups: Explore – Confidence intervals for means M-estimators identification of outliers plotting of findings.

Nonparametric tests – Chi-square, Binomial, Runs, one-sample, two independent samples, k-independent samples, two related samples, k-related samples.

Correlation – Test for bivariate or partial correlation, or for distances indicating similarity or dissimilarity between measures.

ANOVA and ANCOVA – Conduct contrast, range and post hoc tests analyze 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.

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.

Descriptive ratio statistics – Coefficient of dispersion, coefficient of variation, price-related differential and average absolute deviance.

Descriptives – Central tendency, dispersion, distribution and Z scores.

Frequencies – Counts, percentages, valid and cumulative percentages central tendency, dispersion, distribution and percentile values.

Crosstabulations – Counts, percentages, residuals, marginals, tests of independence, test of linear association, measure of linear association, ordinal data measures, nominal by interval measures, measure of agreement, relative risk estimates for case control and cohort studies.

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