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.

Element Name Description
General
Detail of computed results reported Detail of computed results; if Minimal detail is requested, then only Eigenvalues and the final factor loadings are reported; if Comprehensive results is requested, then various additional summary statistics are reported as well, including plots of factor loadings; if All results is requested, then various descriptive statistics, including the input correlation matrix, is also reported. Factor scores can be requested as an option.
MD Deletion Missing data can be deleted Casewise or Pairwise, or missing data can be substituted by the means for the respective variables.
Factor extraction method Specifies the factor extraction method (and type of factor analysis).
Number of factors Specifies number of factors desired or assumed reasonable for the analysis. STATISTICA will extract no more than the number of factors requested in this field, and no more than there are eigenvalues greater than the value specified in the Minimum eigenvalue field. Thus, both this parameter and the Minimum eigenvalue parameter determine the number of factors that will be extracted.
Minimum eigenvalue Specifies the minimum eigenvalue value for a factor to be extracted (retained) for the analysis. STATISTICA will extract no more than the Number of factors requested, and no more than there are eigenvalues greater than the value specified in the Minimum eigenvalue field. Thus, both the Number of factors parameter and the Minimum eigenvalue parameter determine the number of factors that will be extracted.
Factor rotation Specifies the factor rotation for the final solution.
Hierarchical solution Creates hierarchical factor analysis of oblique factors (see Thompson, 1951; Schmid & Leiman, 1957; Wherry, 1959, 1975, 1984); in short, STATISTICA will analyze clusters of marker variables (with high unique factor loadings, and low cross-loadings) and rotate axes through those clusters. If no such clear clusters of 'marker variables' can be identified for each oblique factor, the hierarchical analysis will not be performed. This option is only available if more than one factor was extracted.
Creates factor scores Creates and reports factor scores for each observation.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.
Iterated Communalities
Min. change in communality Specifies a minimum change in communalities over successive iterations for the Centroid and Principal axis extraction method (iterations); Principal axis factoring and centroid factoring procedures will recompute the communalities in successive iterations until some criterion is met. Specifically, iterations will stop when the change in the successively computed communalities is less than the value specified in the Min. change in communality box, or when the Maximum no. of iterations have been exceeded.
Max. number of iterations Specifies the maximum number of iterations for the Centroid and Principal axis extraction method (iterations); Principal axis factoring and centroid factoring procedures will recompute the communalities in successive iterations until some criterion is met. Specifically, iterations will stop when the change in the successively computed communalities is less than the value specified in the Min. change in communality box, or when the Maximum no. of iterations have been exceeded.
Highlights loadings Specifies a value for highlighting large (absolute) factor loadings; this value will be used to highlight factor loadings in the spreadsheets with factor loadings.
Highlights residual correlations Highlights residual correlations (in the optional spreadsheet of residual correlations) with absolute values exceeding the value specified in this field.