Utilizing a few unseen 'factors,' factor analysis is a means to detect associated variables and estimate the differences among them. The concept is to decrease groups of variables in any dataset by examining the already present variables. Due to this, it is mostly applied to ML as a technique for assisting data mining. In areas including finance, biology, psychology, marketing, operational research, and many more factor analysis is applied.
For instance, when asked about a product's utility, cost, and durability during surveys regarding consumer satisfaction, respondents may give similar answers. In every study, there are a similar amount of variables and factors. Every component detects a distinct variation in every observed variable. The amount of variance a factor observes is shown by its "eigenvalue." There is variability in a number of variables on a portion of the factor, as indicated by an eigenvalue greater than 1.
What are the Techniques Used in Factor Analysis?
The commonly used methods for factor analysis are as follows:
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Primary Components Analysis
Starts the process of extracting the largest recorded variance and distributing it among the first factor. The next step is for it to reverse its operation and retrieve the largest variance from the next element in the line. Every factor is subjected to the same procedure. This technique can be utilized to systematically determine the requirements and demand of a targeted market.
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Image Factoring
Uses the correlation matrix as its foundation. It utilizes OLS Regression to determine the variables. This technique can be applied to assess the importance of specific subliminal advertisements.
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Common Variable Analysis
Divides the variables into factors after evaluating and obtaining the predominant variance in the variables. It is mostly utilized in SEM and may not always take into account a particular variance for each variable.
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Maximum Likelihood Method
Uses a correlation metric to operate. The variables are factored in using the maximum likelihood approach in this case. You can use it to determine what customers value most in a product.
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Other Methods
Factoring additionally takes advantage of the weighted square. It makes use of the regression-based methodology.
The Use of Factor Analysis
Here are two instances of factor analysis in action from real-life situations.
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Factor analysis in market research
If you're a marketer you need to find out how satisfied consumers are with a product by surveying the market. It's challenging to quantify satisfaction, and it's even more challenging to identify its causes.
Factor analysis has been utilized in the marketing industry to create things like perception maps and sophisticated SWOT analyses, among other things. It collects undetected factors like customer satisfaction and quantifies their values in comparison to visible indicators like rising sales.
Companies would create a survey that would put a heavy emphasis on inquiries about customer satisfaction. This would involve elements like product sturdiness, packaging, and reusability potential. Following customer survey completion, you would conduct a factor analysis to determine which factors are most important for determining consumer satisfaction by extracting the variation of every variable. For purposes of future analysis, companies can merge responses that are strongly associated across different variables or questions into one factor.
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Factor analysis in investing
You can be a stock trader and intends to put money into the latest initial public offering (IPO) of a technology-focused business. IPOs are inherently unpredictable, thus it is impossible to anticipate whether investing in one would be profitable.
Investments are a dangerous endeavor that heavily relies on data analytics. Diversifying a portfolio helps to reduce threats, but it cannot completely eradicate them and it doesn't guarantee a return. The investment industry is susceptible to a wide range of unfavorable circumstances, including macro- and microeconomic shocks, natural disasters, and many more.
In this situation, you will understand the advantages of factor analysis. Factor analysis would foresee shifts and uncover information that might have been concealed. Consider that you made an IPO investment coupled with additional investment in a business that deals in commodities. Without factor analysis, a shift in the price of an unrelated variable, like gasoline, might not be adequately compared in this case. You will be able to determine the effectiveness of the IPO in question after doing a complete exploratory factor analysis. For example, Factor Analysis helps to understand the reasons for the increase in demand for civil helicopters in the near future.
Conclusion
Better research data, more precise statistical analysis, and the derivation of intangible elements that can only be quantified through careful analysis are all advantages of factor analysis. Reliable data and research are crucial for success in today's saturated society.
Author's Detail:
Kalyani Raje / Linkedin
With a work experience of over 10+ years in the market research and strategy development. I have worked with diverse industries, including FMCG, IT, Telecom, Automotive, Electronics and many others. I also work closely with other departments such as report writing, content writing, product development, and marketing to understand customer needs and preferences, and develop strategies to meet those needs.
Author's Detail:
Kalyani Raje /
LinkedIn
With a work experience of over 10+ years in the market research and strategy development. I have worked with diverse industries, including FMCG, IT, Telecom, Automotive, Electronics and many others. I also work closely with other departments such as sales, product development, and marketing to understand customer needs and preferences, and develop strategies to meet those needs.
I am committed to staying ahead in the rapidly evolving field of research and analysis. This involves regularly attending conferences, participating in webinars, and pursuing additional certifications to enhance my skill set. I played a crucial role in conducting market research and competitive analysis. I have a proven track record of distilling complex datasets into clear, concise reports that have guided key business initiatives. Collaborating closely with multidisciplinary teams, I contributed to the development of innovative solutions grounded in thorough research and analysis.
