Three methods of factor analyzing dichotomously scored item performance data were compared using two raw score data sets of 20-item tests, one reflecting normally distributed latent traits and the other reflecting uniformly distributed latent traits. This comparison was accomplished by using phi and tetrachoric correlations among dichotomous data and Pearson product-moment correlations among Rasch probability estimates of the same dichotomous data in factor analysis. A sample size of 324 resulted from 36 persons at each of nine score values. Eigenvalues for the phi, tetrachoric, and Rasch-r correlation matrices derived from each of the data sets were computed. The Rasch approach, as a psychometric measurement model, was chosen because it met the assumption of a linear ability continuum underlying dichotomous item response data. Results illustrate the superiority of the Rasch-based technique for factor analyzing dichotomously scored item response data. Six tables, one figure, and a 34-item list of references are included. (SLD)