Exploratory factor analysis and principal component analysis in clinical studies: Which one should you use?

Factor analysis covers a range of multivariate methods used to explain how underlying factors influence a set of observed variables. When research aims to identify these underlying factors, exploratory factor analysis (EFA) is used. In contrast, when the aim is to test whether a set of observed variables represents the underlying factors, in accordance with an existing conceptual basis, confirmatory factor analysis is performed. EFA has many similarities with a commonly used data reduction technique called principal component analysis (PCA). These similarities, along with using the related terms factor and component interchangeably, contribute to confusion in analysis. The difficulty in identifying the appropriate use of statistical methods and their application and interpretation impacts clinical and research implications (Beavers et al., 2013; Tabachnick Watson Gorsuch, 2014). The relationship between an observed variable and a component/factor is expressed by a factor loading (ranging from 0 to 1), which measures the amount of the variance in the variable explained by the component/factor. A factor loading of >0.4 generally indicates that the variable can be attributed to the factor (Cutillo, 2019). A factor loading matrix shows the relationship between the factors and the original variables, with components/factors typically named by the common attributes of the set of variables with which they are most correlated. Neither EFA nor PCA provide a unique solution, as component/factor rotation allows for an infinite number of possible representations. The rotation can be chosen to maximize simplicity, interpretation, and/or replicability (Fabrigar, Wegener, MacCallum, (b) limited contact with family and relatives; (c) stigma of being in hospital; and (d) sleeplessness due to noisy rooms, PCA may have identified two principal components physical agents and psychosocial agents representing the four variables. The left side of Figure 1 shows PCA as a data reduction process identifying two principal components, whereas the right side shows EFA as a structure identification process comprising two latent factors. Exploratory factor analysis and PCA are related but conceptually distinct techniques (Basto Hooper, 2012). An awareness of the differences between PCA and EFA allows for alignment between statistical approach and research objectives, and ensures appropriate interpretation of results (Santos et al., 2019). Both EFA and PCA procedures identify patterns regardless of clinical knowledge behind those variables. These procedures can be used when the researcher has limited information with regard to the latent structure (Lever, Krzywinski, Pett, Lackey, Chao, Wu, Wu, & Chen, 2018). Although both EFA and PCA are similar, they have different applications and interpretation. EFA is used to understand the underlying factors that are responsible for a set of observed variables, whereas PCA is used when the aim is data reduction. Given the problematic nature of causal language, a careful consideration of statistical procedure choice and research evidence reporting is important to minimize misinterpretation to better support the veracity of knowledge development (Thapa, Visentin, Hunt, Watson, & Cleary, 2020). As EFA and PCA have conceptual and statistical differences, attention to their characteristics is required to support accurate use and reporting so keep this in mind when you are deciding which one to use for your data needs. No conflict of interest was declared by the authors in relation to the editorial itself. Note that Roger Watson is JAN Editor-in-Chief.