The FACTOR Procedure
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Overview
- Getting Started
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Syntax
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Details
Input Data Set Output Data Sets Confidence Intervals and the Salience of Factor Loadings Simplicity Functions for Rotations Missing Values Cautions Factor Scores Variable Weights and Variance Explained Heywood Cases and Other Anomalies about Communality Estimates Time Requirements Displayed Output ODS Table Names ODS Graphics
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Examples
- References
| Missing Values |
If the DATA= data set contains data (rather than a matrix or factor pattern), then observations with missing values for any variables in the analysis are omitted from the computations. If a correlation or covariance matrix is read, it can contain missing values as long as every pair of variables has at least one nonmissing entry. Missing values in a pattern or scoring coefficient matrix are treated as zeros.