Effects of under- and overextraction on principal axis factor analysis with varimax rotation

The effects of under- and overextraction on principal axis factor analysis with varimax rotation were examined in 2 Monte Carlo studies involving 6,420 factor analyses. It was found that (a) when underextraction occurs. the estimated factors are likely to contain considerable error; (b) when overextraction occurs, the estimated loadings for true factors usually contain substantially less error than in the case of underextraction; and (c) overextraction can result in factor splitting when a general factor is present and there are no unique variables in the data set. The authors recommend that factor analysts (a) use effective methods to estimate the number of factors; (b) avoid underextraction, even at the risk of overextraction; and (c) include randomly generated unique variables as ''insurance'' against factor splitting when a general factor may be present.