A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis

Abstract

The following paper discusses exploratory factor analysis and gives an overview of the statistical technique and how it is used in various research designs and applications. A basic outline of how the technique works and its criteria, including its main assumptions are discussed as well as when it should be used. Mathematical theories are explored to enlighten students on how exploratory factor analysis works, an example of how to run an exploratory factor analysis on SPSS is given, and finally a section on how to write up the results is provided. This will allow readers to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output. The broad purpose of factor analysis is to summarize data so that relationships and patterns can be easily interpreted and understood. It is normally used to regroup variables into a limited set of clusters based on shared variance. Hence, it helps to isolate constructs and concepts. Note that both Sean Pearce and An Gie Yong should be considered as first authors as they contributed equally and substantially in the preparation of this manuscript. The authors would like to thank Dr. Louise Lemyre and her team, Groupe d’Analyse Psychosociale Santé (GAP-Santé), for their generous feedback; in particular, Dr. Lemyre who took the time to provide helpful suggestions and real world data for the tutorial. The authors would also like to thank Levente Orbán and Dr. Sylvain Chartier for their guidance. The original data collection was funded by PrioNet