International Journal of Research and Scientific Innovation (IJRSI) | Volume VI, Issue IX, September 2019 | ISSN 2321–2705

Oblique versus Orthogonal Rotation in Exploratory Factor Analysis

Kimani Chege Gabriel

Moi University – Kenya

Abstract: – Exploratory factor analysis is widely applied by psychometricians and other behavioural science researchers in complex studies involving numerous variables and factors. A variable might be related to more than one factor and therefore a psychometrician should consider this possibility when deciding about how many factors will be considered when analysing the data. The rotation of factors is used to get more interpretable and simplified solutions from the factor extraction results by maximising high item loadings and minimising low item loadings. Rotation helps to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables. It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality. However, practitioners and researchers often make questionable decisions when conducting these analyses, especially in the choice of the rotation method from among the two; orthogonal and oblique.This paper therefore sought to examine exploratory factor analysis and its relevant protocol, discusses the two factor rotation methods, the operational differences and the parsimoneity of outputs, eigenvectors which are usually at the center of rotation as well as a guide for practitioners in deciding between orthogonal and oblique rotation. Finally the paper gives a parting short in the conclusion section. It is hoped that the paper will present useful insights for practitioners’ use.

I. INTRODUCTION

Factor analysis is a psychometric technique that is used to reduce a large number of variables into fewer numbers of factors. Technically speaking, the technique extracts maximum common variance from all variables and puts them into a common score. In simple terms, factor analysis is a way to take a mass of data and shrinking it to a smaller data set that is more manageable and more understandable. In its internal functioning, it seeks to find hidden patterns, show how those patterns overlap and show what characteristics are seen in multiple patterns.
This paper focused on Exploratory Factor Analysis (EFA) which is a type of factor analysis that is used to find the underlying structure of a large set of variables. It reduces data to a much smaller set of summary variables.