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

An Gie Yong,Sean Pearce

doi:10.20982/tqmp.09.2.p079

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.

Highlights

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
Factor analysis operates on the notion that measurable and observable variables can be reduced to fewer latent variables that share a common variance and are unobservable, which is known as reducing dimensionality (Bartholomew, Knott, & Moustaki, 2011)
A basic hypothesis of Exploratory Factor Analysis (EFA) is that there are m common ‘latent’ factors to be discovered in the dataset, and the goal is to find the smallest number of common factors that will account for the correlations (McDonald, 1985)

Summary

A Beginner’s Guide to Factor Analysis

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. 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 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. CFA attempts to confirm hypotheses and uses path analysis diagrams to represent variables and factors, whereas EFA tries to uncover complex patterns by exploring the dataset and testing predictions (Child, 2006). This tutorial will be focusing on EFA by providing fundamental theoretical background and practical SPSS techniques.

A Look at Exploratory Factor Analysis

Limitations

A useful summary of extraction methods can be found in

Rotation Methods

A summary of the rotation techniques can be found in

Findings

Conclusion