What Are Principal Components Analysis and Exploratory Factor Analysis?
Principal components analysis (PCA) and exploratory factor analysis (EFA) are often referred to collectively as factor analysis (FA). The general notion of FA includes “a variety of statistical techniques whose common objective is to represent a set of variables in terms of a smaller number of hypothetical variables” (Kim and Mueller, 1978, p. 9). A more elaborate definition is provided by Tabachnick and Fidell (2007, p. 607): … statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the set form coherent subsets that are relatively independent of one another. Variables that are correlated with one another but largely independent of other subsets of variables are combined into factors. In the study you mentioned, Lee and Kim (2008) looked at the attitudes expressed by 111 heritage and traditional learners of Korean, and then performed a PCA (with varimax rotation) on the results. The participants answered a 34-item
