What is Factor Analysis?

History and Definition

In 1904, Charles Spearman first used Factor Analysis in the field of psychology when he suggested that the performance of school children on a large number of subjects was linearly related to a common underlying factor (which he called g, hence ‘g Theory’) that defined general intelligence.

Later, Raymond Cattel used Factor Analysis to formulate his famous 16 Factor Model of Personality to explain intelligence. He was also a strong believer in the use of statistical tools and psychometrics to provide the base of theories in psychology rather than just basing them on verbal arguments and discussion.

Factor Analysis is a statistical tool that measures the impact of a few un-observed variables called factors on a large number of observed variables. It is used as a data reduction method. It may be used to uncover and establish the cause and effect relationship between variables or to confirm a hypothesis. It is often used to determine a linear relationship between variables before subjecting them to further analysis.

Principal Factor Analysis is also called Common Factor Analysis and it aims to identify the minimum number of factors that can lead to the correlation between a given set of variables. Other types of Factor Analysis include Image factoring, Alpha factoring, Principal Component Analysis and so on.

Illustration and Benefits of Factor Analysis

Say you are a retailer and want to increase customer footfalls through brand promotions and a better understanding of customer’s purchase behavior. You could effectively use Factor Analysis to provide you with deep insights on customer demographics and buying behavior. This would help you target your market better and achieve higher sales.

There are many benefits of using Factor Analysis vis-á-vis other statistical tools. Firstly, it is inexpensive and simple to use and can be used in a wide variety of situations. Secondly, it can be used to identify a lot of underlying dormant factors that other tools may not be able to highlight. Thirdly, as long as it is possible to assign scores to subjective attributes, even they can be used extensively in Factor Analysis.

R is an open source programming package often used for statistical analysis and we can use it to conduct R Factor Analysis as well. R can be used to conduct exploratory factor analysis as well as confirmatory factor analysis.

SPSS is another statistical software package used widely to conduct Factor Analysis. We have a lot of flexibility and freedom to easily choose from various options while conducting SPSS Factor Analysis – we may choose among various methods of factor extraction, choose between covariance and correlation matrix, select from among the various methods of factor rotations, etc.

Practical Applications of Factor Analysis

Factor Analysis has been successfully used in a wide variety of industries and fields. Its use was pioneered in the field of psychology where it still used in various studies to identify what factors influence intelligence, attitudes, behaviors, etc. Apart from psychology, it is an extremely useful tool in the field of various physical sciences to identify factors affecting availability and location of underground resources and minerals, water quality or weather patterns.

Factor Analysis is also extensively used in the field of marketing and market research related to product attributes and perceptions. The construction of ‘Perceptual Maps’ and product positioning studies are some crucial areas where Factor Analysis is widely used along with other quantitative research and analysis tools.

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