Factor analysis is used to identify relationships between measured variables by grouping them into factors based on patterns of correlation (Tavakol & Wetzel, 2020). It helps reveal the structure of complex data by reducing a large number of variables into a smaller set of latent constructs (Tavakol & Wetzel, 2020).
Example of Assessment Instrument
The Beck Depression Inventory-II (BDI-II) is a self-report measure designed to examine the severity of depression (Gray et al., 2023; Lee et al., 2017). Factor analytic studies have consistently demonstrated that the BDI-II contains two or more delineated factors or subscales, typically reflecting cognitive-affective and somatic dimensions of depressive symptoms (Gray et al., 2023; Lee et al., 2017). These subscales have been validated through both exploratory and confirmatory factor analyses, supporting their use in clinical and research settings to understand better symptom profiles (Gray et al., 2023; Lee et al., 2017).
Exploratory and Confirmatory Factor Analysis
Exploratory Factor Analysis (EFA) is employed when the researcher lacks a predetermined understanding of the number of factors or which variables load onto which factors—it is a data-driven approach to uncover the underlying structure (Jiang et al., 2023; Tavakol & Wetzel, 2020). Confirmatory Factor Analysis (CFA), in contrast, is hypothesis-driven and used to test whether the data fit a specified factor structure, typically one that is grounded in theory or previous research (Jiang et al., 2023; Tavakol & Wetzel, 2020). EFA is often used in the early stages of scale development, while CFA is used to confirm and validate the proposed structure (Jiang et al., 2023; Tavakol & Wetzel, 2020).
References
Gray, J. S., Petros, T., & Stupnisky, R. (2023). Confirmatory factor analysis of Beck Depression Inventory-II with two American Indian samples. The American Journal of Orthopsychiatry, 93(4), 316–320. https://doi.org/10.1037/ort0000672
Jiang, G., Tan, X., Wang, H., Xu, M., & Wu, X. (2023). Exploratory and confirmatory factor analyses identify three structural dimensions for measuring physical function in community-dwelling older adults. PeerJ, 11, e15182. https://doi.org/10.7717/peerj.15182