Factor Analysis in education research using R

Factor analysis is a method commonly employed to reduce a large number of variables into fewer numbers of factors. The method is often used to identify which observable indicators are representative of latent, not directly-observed constructs. This is a key step in developing valid instruments to assess latent constructs in educational research (e.g., student engagement or motivation). The chapter describes the two main approaches for conducting factor analysis (and how to combine them in an integrated factor analysis strategy) and provides a tutorial on implementing both techniques in the R programming language. The first is confirmatory factor analysis (CFA), a more theory-driven approach, in which a researcher actively specifies the number of underlying constructs as well as the pattern of relations between these dimensions and observed variables. The second is exploratory factor analysis (EFA), a more data-driven approach, in which the number of underlying constructs is inferred from the data, and all underlying constructs are assumed to influence all observed variables (at least to some degree).