مرکز منطقه ای اطلاع رساني علوم و فناوري - The Comparison of Factor Extraction Methods Used in Exploratory Factor Analysis

Abstract :

The purpose of this study was to compare factor extraction methods used in exploratory factor analysis in terms of variance and factor number according to sample size and the communality level conditions. In this simulation study, data were generated with respect to two levels of sample sizes (100, 500), three levels of communality (high, wide and low), with 20 variables, 3 factors, and 1000 replications by using SAS program. As a resut of comparisons, it was found that total explained variance increased when sample size increased for data with high or low communality levels. However, total explained variance decreased when sample size increased, for the data with wide communality level. Component analysis yielded the biggest total explained variance compared to other methods, whereas image analysis yielded the lowest explained variance. The rest of the factor extracting methods provided the similar results to each other. Component analysis produced the biggest factor loadings and image analysis yielded the lowest factor loadings and there was no difference concerning factor loadings for the remaining methods. For the data generated with high and wide communality levels, no difference was observed among the extraction methods in terms of number of factors. However, for data generated with low communality level image analysis didn’t extract three factors. Hence, image analysis is not recommended for data with low communality level and low sample sizes.