The FACTOR Procedure
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Overview
- Getting Started
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Syntax
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DetailsInput Data SetOutput Data SetsConfidence Intervals and the Salience of Factor LoadingsSimplicity Functions for RotationsMissing ValuesCautionsFactor ScoresVariable Weights and Variance ExplainedHeywood Cases and Other Anomalies about Communality EstimatesTime RequirementsDisplayed OutputODS Table NamesODS 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.