Factor analysis and component analysis represent two broad classes of methods employed generally with similar types of problems. The purpose of the present study is to determine the extent to which and under what conditions the methods produce different patterns. Principal component analysis, image component analysis, and maximum likelihood factor analysis were performed on simulated data matrices. Comparisons were made between each of the three methods and to ideal patterns. Sample size, saturation, and type of pattern were systematically varied. The general conclusion reached is that the three methods produce results that are equivalent. In addition, several important trends were observed.
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