Section author: Danielle J. Navarro and David R. Foxcroft

Summary

In this chapter on factor analysis and related techniques we have introduced and demonstrated statistical analyses that assess the pattern of relationships in a data set. Specifically, we have covered:

• Exploratory Factor Analysis (EFA). EFA is a statistical technique for identifying underlying latent factors in a data set. Each observed variable is conceptualised as representing the latent factor to some extent, indicated by a factor loading. Researchers also use EFA as a way of data reduction, i.e. identifying observed variables than can be combined into new factor variables for subsequent analysis.
• Principal Component Analysis (PCA) is a data reduction technique which, strictly speaking, does not identify underlying latent factors. Instead, PCA simply produces a linear combination of observed variables.
• Confirmatory Factor Analysis (CFA). Unlike EFA, with CFA you start with an idea - a model - of how the variables in your data are related to each other. You then test your model against the observed data and assess how good a fit the model is to the data.
• Internal consistency reliability analysis. This form of reliability analysis tests how consistently a scale measures a measurement (psychological) construct.