Principal component analysis, image component analysis, and maximum likelihood factor analysis were compared to assess the effects of variable sampling. Results with respect to degree of saturation and average number of variables per factor were clear and dramatic. Differential effects on boundary cases and nonconvergence problems were also found.…

Effects of overextracting factors and components within and between maximum likelihood factor analysis and principal components analysis were examined through computer simulation of a range of factor and component patterns. Results demonstrate similarity of component and factor scores during overextraction. Overall, results indicate that…

Situations for which the researcher should use component analysis versus common factor analysis are discussed. Topics addressed include key algebraic similarities and differences, theoretical and practical issues, the factor indeterminacy issue, latent versus manifest variables, and differences between exploratory and confirmatory analysis…

Principal component, image component, three types of factor score estimates, and one scale score method were compared over different levels of variables, saturations, sample sizes, variable to component ratios, and pattern rotations. There were virtually no overall differences among methods, with the average correlation between matched scores…

Factor analysis, image analysis, and principal component analysis are compared with respect to the factor patterns they would produce under various conditions. The general conclusion that is reached is that the three methods produce results that are equivalent. (Author/JKS)