An Introduction to Factor Analysis

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An Introduction to Factor Analysis

Hi Guys, in this article I will be giving a theoretical overview about Factor Analysis.

 This article could also be considered as a continuation of my previous article on Structural Equation Modelling. Since a prior knowledge on Factor analysis is very important to have a better understanding of SEM, I would like to give separate and dedicated article related to Factor Analysis and Path Analysis. This article will cover the Factor Analysis and soon there will be a separate one for Path Analysis too.

Lets get started,

What exactly a Factor Analysis is?

The theory behind the factor analysis is that, the information gained about the inter-dependencies between observed variables can be used later to reduce the set of variables in a dataset. In general, it can be considered as a Data reduction or Dimensionality reduction technique. It's objective is to find out the latent factors that create a commonality among the set of variables.

The key concept of factor analysis is that multiple observed variables have similar patterns of responses because they are all associated with a latent variable and thus it allows researchers to investigate concepts, that are not measured directly, by collapsing the large number of variables into a few interpretable underlying factors.

The purpose of factor analysis is to reduce individual items into a fewer number of dimensions.Factor analysis is an inter-dependence technique. The complete set of inter-dependent relationships is examined.

One more important terminology which all of us would come across during our research related to Factor analysis is "Factor Rotation".On our further research to understand the concept of Factor rotation, we might have felt it too tedious.

I hope the following lines will help you to have a better understanding of the purpose of Factor Rotation.

Factor rotations are done for the sake of interpretation of the extracted factors in factor analysis. Rotations minimize the complexity of the factor loadings to make the structure simpler to interpret.

There are two types of rotations:

Orthogonal rotations: It constrains the factors to be uncorrelated. In many cases it is unrealistic to expect the factors to be uncorrelated, and forcing
them to be uncorrelated makes it less likely that the rotation produces a solution with a simple structure. Ex. Varimax method.

Oblique rotations: It permits the factors to be correlated with one another. Often produces solutions with a simpler structure. Ex. Promax method.

Well now, the very common doubt will be to prefer either Orthogonal rotation method or Oblique rotation methond. Most of us would be completely intrigued at this situation. Fortunately the answer is so simple. It completely depends upon the field of your study. Making sure that the factors
should be "Correlated" or "Uncorrelated" in the field of your study gives the best answer. If the factors has to be correlated, prefer Oblique rotation method and if it has to be uncorrelated, prefer the Orthogonal rotation method.


The following lines will give you a basic overview about the steps involved in implementing a Factor Analysis

Step-1: The correlation matrix - Computation of correlation matrix for all the variables
Step-2: Factor Extraction - Determining the factors
Step-3: Factor Rotation - Factors are rotated to make them more meaningful and easier to interpret.
Step-4: Making final decisions - Finalising the number of factors.


Note:
Eventhough Principal Component Analysis(PCA) comes under the hierarchy of Factor Analysis, there is a slight differentiation from each other.In Factor analysis, the researcher makes the assumption that an underlying causal model exists, whereas PCA is simply a variable reduction technique.The aim of PCA is to determine a few linear combinations of the original variables that can be used to summarize the dataset without losing much information. In my upcoming articles, I will give a detailed discussion about the PCA and how it is related with Factor Analysis.

Thankyou for reading this article. Hope this will give you a better idea about Factor Analysis. This entire article has been drafted focusing the beginners. So, only the basic idea about the Factor Analysis has been discussed, in order to make sure that at no point this article becomes very complex for the readers to understand.

I'll always welcome and value your suggestions. So, please feel free to reach out to me. I'm reachable through the following links.

Email - kgfahath@gmail.com
LinkedIn - www.linkedin.com/in/fahath-kg
Twitter - https://twitter.com/kgfahath
Facebook - https://www.facebook.com/people/Fahath-KG/100010754347966

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