A Method for Removing Outliers to Improve Factor Analytic Results

Multivariate Behavioral Research
A L Comrey

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.

Citations

Feb 5, 1998·Epidemiologia e psichiatria sociale·M Leese
Apr 1, 1988·Multivariate Behavioral Research·J L Rasmussen
Dec 1, 1995·Journal of Autism and Developmental Disorders·J A SevinD A Benavidez
Jul 4, 2006·Child Maltreatment·Martine HébertMarc Tourigny
Jan 1, 2007·Criminal Justice and Behavior·Marc T Swogger, David S Kosson
Jan 1, 2008·Criminal Justice and Behavior·Marc T SwoggerDavid S Kosson
Apr 1, 1995·Multivariate Behavioral Research·D R Bacon
Jan 16, 2020·International Journal of Environmental Research and Public Health·María de Fátima Poza-VilchesMaría Teresa Pozo-Llorente

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