Abstract

Bad data due to faked responses, errors, and other difficulties can distort correlations among variables leading to poor factor analytic results based on matrices of such correlations. A method of detecting potentially bad data cases, or outliers, is presented which is based on the average squared deviation of a given subject's cross product of standard scores from the average over all correlations in the matrix. Results of applying both this program and the BMD 10M outlier program to the same data examples are given. About 40 to 60 percent of the cases identified as outliers by the two programs were the same cases. Many cases identified as outliers proved not to be "bad data", however, so these programs should be used to identify cases that need scrutiny rather than as the sole basis for eliminating data.