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We present an introduction to the basic concepts essential to understanding confirmatory factor analysis (CFA). We initially discuss the underlying mathematical model and its graphical representation. We then show how parameters are estimated for the CFA model based on the maximum likelihood function. Finally, we discuss several ways in which model fit is evaluated as well as introduce the concept of model identification. In our presentation, we use an example to illustrate the application of CFA to psychosomatic research and touch on the more general role of structural equation modeling in psychosomatic research. SEM = structural equation modeling; CFA = confirmatory factor analysis; CAD = coronary artery disease; EFA = exploratory factor analysis; CFI = comparative fit index; RMSEA = root mean square error of approximation.
Babyak et al. (2010) studied this question.