What is Factor Analysis?
Data is everywhere. From data research to artificial intelligence technology, data has become an essential commodity that is being perceived as a link between our past and future. Is an organization willing to collect its past records?
Data is the key solution to this problem. Is any programmer willing to formulate a Machine Learning algorithm? Data is what s/he needs to begin with.
While the world has moved on to technology, it still is unaware of the fact that data is the building block of all these technological advancements that have together made the world so advanced.
When it comes to data, a number of tools and techniques are put to work to arrange, organize, and accumulate data the way one wants to. Factor Analysis is one of them. A data reduction technique, Factor Analysis is a statistical method used to reduce the number of observed factors for a much better insight into a given dataset.
But first, we shall understand what is a factor. A factor is a set of observed variables that have similar responses to an action. Since variables in a given dataset can be too much to deal with, Factor Analysis condenses these factors or variables into fewer variables that are actionable and substantial to work upon.
A technique of dimensionality reduction in data mining, Factor Analysis works on narrowing the availability of variables in a given data set, allowing deeper insights and better visibility of patterns for data research.
Most commonly used to identify the relationship between various variables in statistics, Factor Analysis can be thought of as a compressor that compresses the size of variables and produces a much enhanced, insightful, and accurate variable set.
“FA is considered an extension of principal component analysis since the ultimate objective for both techniques is a data reduction.”Factor Analysis in Data Reduction
Types of Factor Analysis
Developed in 1904 by Spearman, Factor Analysis is broadly divided into various types based upon the approach to detect underlying variables and establish a relationship between them.
While there are a variety of techniques to conduct factor analysis like Principal Component Analysis or Independent Component Analysis, Factor Analysis can be divided into 2 types which we will discuss below. Let us get started.
Confirmatory Factor Analysis
As the name of this concept suggests, Confirmatory Factor Analysis (CFA) lets one determine whether a relationship between factors or a set of overserved variables and their underlying components exists.
It helps one confirm whether there is a connection between two components of variables in a given dataset. Usually, the purpose of CFA is to test whether certain data fit the requirements of a particular hypothesis.
The process begins with a researcher formulating a hypothesis that is made to fit along the lines of a certain theory. If the constraints imposed on a model do not fit well with the data, then the model is rejected, and it is confirmed that no relationship exists between a factor and its underlying construct. Perhaps hypothetical testing also finds a space in the world of Factor Analysis.
Exploratory Factor Analysis
In the case of Exploratory Factor Statistical Analysis, the purpose is to determine/explore the underlying latent structure of a large set of variables. EFA, unlike CFA, tends to uncover the relationship, if any, between measured variables of an entity (for example - height, weight, etc. in a human figure).
While CFA works on finding a relationship between a set of observed variables and their underlying structure, this works to uncover a relationship between various variables within a given dataset.
Conducting Exploratory Factor Analysis involves figuring the total number of factors involved in a dataset.
“EFA is generally considered to be more of a theory-generating procedure than a theory-testing procedure. In contrast, confirmatory factor analysis (CFA) is generally based on a strong theoretical and/or empirical foundation that allows the researcher to specify an exact factor model in advance.”EFA in Hypothesis Testing
Applications of Factor Analysis
With immense use in various fields in real life, this segment presents a list of applications of Factor Analysis and the way FA is used in day-to-day operations.
Applications of factor analysis
Marketing is defined as the act of promoting a good or a service or even a brand. When it comes to Factor Analysis in marketing, one can benefit immensely from this statistical method.
In order to boost marketing campaigns and accelerate success, in the long run, companies employ Factor Analysis techniques that help to find a correlation between various variables or factors of a marketing campaign.
Moreover, FA also helps to establish connections with customer satisfaction and consequent feedback after a marketing campaign in order to check its efficacy and impact on the audiences.
That said, the realm of marketing can largely benefit from Factor Analysis and trigger sales with respect to much-enhanced feedback and customer satisfaction reports.
(Must read: Marketing management guide)
In data mining, Factor Analysis can play a role as important as that of artificial intelligence. Owing to its ability to transform a complex and vast dataset into a group of filtered out variables that are related to each other in some way or the other, FA eases out the process of data mining.
For data scientists, the tedious task of finding relationships and establishing correlation among various variables has always been full of obstacles and errors.
However, with the help of this statistical method, data mining has become much more advanced.
(Also read: Data mining tools)
Machine Learning and data mining tools go hand in hand. Perhaps this is the reason why Factor Analysis finds a place among Machine Learning tools and techniques.
As Factor Analysis in machine learning helps in reducing the number of variables in a given dataset to procure a more accurate and enhanced set of observed factors, various machine learning algorithms are put to use to work accordingly.
They are trained well with humongous data to rightly work in order to give way to other applications. An unsupervised machine learning algorithm, FA is largely used for dimensionality reduction in machine learning.
Thereby, machine learning can very well collaborate with Factor Analysis to give rise to data mining techniques and make the task of data research massively efficient.
(Recommended blog: Data mining software)
Nutritional Science is a prominent field of work in the contemporary scenario. By focusing on the dietary practices of a given population, Factor Analysis helps to establish a relationship between the consumption of nutrients in an adult’s diet and the nutritional health of that person.
Furthermore, an individual’s nutrient intake and consequent health status have helped nutritionists to compute the appropriate quantity of nutrients one should intake in a given period of time.
The application of Factor Analysis in business is rather surprising and satisfactory.
Remember the times when business firms had to employ professionals to dig out patterns from past records in order to lay a road ahead for strategic business plans?
Well, gone are the days when so much work had to be done. Thanks to Factor Analysis, the world of business can use it for eliminating the guesswork and formulating more accurate and straightforward decisions in various aspects like budgeting, marketing, production, and transport.
Pros and Cons of Factor Analysis
Having learned about Factor Analysis in detail, let us now move on to looking closely into the pros and cons of this statistical method.
Pros of Factor Analysis
The first and foremost pro of FA is that it is open to all measurable attributes. Be it subjective or objective, any kind of attribute can be worked upon when it comes to this statistical technique.
Unlike some statistical models that only work on objective attributes, Factor Analysis goes well with both subjective and objective attributes.
While data research and data mining algorithms can cost a lot due to the extraordinary charges, this statistical model is surprisingly cost-effective and does not take many resources to work with.
That said, it can be incorporated by any beginner or an experienced professional in light of its cost-effective and easy approach towards data mining and data reduction.
While many machine learning algorithms are rigid and constricted to a single approach, Factor Analysis does not work that way.
Rather, this statistical model has a flexible approach towards multivariate datasets that let one obtain relationships or correlations between various variables and their underlying components.
(Must read: AI algorithms)
Cons of Factor Analysis
While there are many pros of Factor Analysis, there are various cons of this method as well. Primarily, Factor Analysis can procure incompetent results due to incomprehensive datasets.
While various data points can have similar traits, some other variables or factors can go unnoticed due to being isolated in a vast dataset. That said, the results of this method could be incomprehensive.
Non-Identification of Complicated Factors
Another drawback of Factor Analysis is that it does not identify complicated factors that underlie a dataset.
While some results could clearly indicate a correlation between two variables, some complicated correlations can go unnoticed in such a method.
Perhaps the non-identification of complicated factors and their relationships could be an issue for data research.
Reliant on Theory
Even though Factor Analysis skills can be imitated by machine learning algorithms, this method is still reliant on theory and thereby data researchers.
While many components of a dataset can be handled by a computer, some other details are required to be looked into by humans.
Thus, one of the major drawbacks of Factor Analysis is that it is somehow reliant on theory and cannot fully function without manual assistance.
(Suggested reading: Deep learning algorithms)
To sum up, Factor Analysis is an extensive statistical model that is used to reduce dimensions of a given dataset with the help of condensing observed variables in a smaller size.
(Top reading: Statistical data distribution models)
By arranging observed variables in groups of super-variables, Factor Analysis has immensely impacted the way data mining is done. With numerous fields relying on this technique for better performance, FA is the need of the hour.