15. Factor Analysis

This chapter provides an in-depth exploration of Factor Analysis (FA), a statistical approach used to examine the relationships among multiple variables to uncover underlying latent factors. Latent factors represent unobservable constructs inferred from observed data, making FA an essential tool for researchers in psychology and behavioural sciences. The chapter begins with Exploratory Factor Analysis (EFA), a technique for identifying hidden patterns in data by examining how observed variables co-vary. The discussion covers critical steps such as assumption checks, factor extraction, rotation methods, and interpretation of results, supported by practical examples using jamovi. The chapter also introduces Principal Component Analysis (PCA) as a related data reduction method, clarifying its distinctions from EFA.

Advancing to Confirmatory Factor Analysis (CFA), the chapter explains how researchers test hypothesised models against observed data to validate latent structures. A detailed walkthrough of the Multi-Trait Multi-Method CFA (MTMM CFA) approach showcases its utility in accounting for both latent factor and method variance. The chapter concludes with a discussion on internal consistency reliability analysis, focusing on evaluating how consistently a scale measures its intended psychological construct using metrics like Cronbach’s alpha and McDonald’s omega. Together, these techniques provide a robust framework for understanding and analysing complex data structures, enabling researchers to derive meaningful insights into latent constructs and their measurement.