Multivariate Exploratory Techniques

  • Cluster Analysis
  • Factor Analysis
    Complete implementation of factor analysis and principal components analysis techniques. Various extraction methods are available, including maximum likelihood; a large number of rotational strategies to approximate simple structure for the final solution are supported; hierarchical factor analysis of oblique factors is also available.
  • Principal Components and Classification Analysis
    Complete implementation of Principal Components Analysis for classification of variables and cases (observations); Statistica computes Principal Components for selected variables and observations, and applies the resultant factor structure to map supplementary variables and observations into the same space (see also Jambu, 1991 for additional details). These computations can be based on the covariance matrix or correlation matrix. Various results spreadsheets and graphs are available to review the mapping of the variables and observations.
  • Canonical Analysis
    Creates a canonical correlation analysis for two lists of continuous variables.
  • Reliability and Item Analysis
    Standard reliability and item analysis for scale (test) development; applies the classical testing model to the construction of sum scales. Statistica computes reliability estimates (Cronbach, split-half), correction of correlations for attenuation, and provides a number of other options useful in scale (test) construction.
  • Classification Trees
  • Correspondence Analysis (CA)
    Complete implementation of simple Correspondence Analysis (CA); see also the separate option for Multiple Correspondence Analysis (MCA). Various methods for specifying input data are supported, including the direct input of tables.
  • Multidimensional Scaling
    Complete implementation of non-metric multidimensional scaling. Statistica expects as input a similarity (e.g., correlation) or dissimilarity matrix.
  • Standard Discriminant Analysis
    Creates a standard discriminant function analysis, and computes various classification statistics. For best-subset selection of predictor effects in ANCOVA-like designs, see the General Discriminant Function Analysis (GDA) facilities.
  • General Discriminant Analysis Models