A GUIDE FOR APPLYING PRINCIPAL-COMPONENTS ANALYSIS AND CONFIRMATORY FACTOR-ANALYSIS TO QUANTITATIVE ELECTROENCEPHALOGRAM DATA

Principal-components analysis (PCA) has been used in quantitative elec troencephalogram (qEEG) research to statistically reduce the dimension ality of the original qEEG measures to a smaller set of theoretically meaningful component variables. However, PCAs involving qEEG have freq uently been performed with small sample sizes, producing solutions tha t are highly unstable. Moreover, solutions have not been independently confirmed using an independent sample and the more rigorous confirmat ory factor analysis (CFA) procedure. This paper was intended to illust rate, by way of example, the process of applying PCA and CFA to qEEG d ata. Explicit decision rules pertaining to the application of PCA and CFA to qEEG are discussed. In the first of two experiments, PCAs were performed on qEEG measures collected from 102 healthy individuals as t hey performed an auditory continuous performance task. Component solut ions were then validated in an independent sample of 106 healthy indiv iduals using the CFA procedure. The results of this experiment confirm ed the validity of an oblique, seven component solution. Measures of i nternal consistency and test-retest reliability for the seven componen t solution were high. These results support the use of qEEG data as a stable and valid measure of neurophysiological functioning. As measure s of these neurophysiological processes are easily derived, they may p rove useful in discriminating between and among clinical (neurological ) and control populations. Future research directions are highlighted.