What Is Factor Analysis? (With Its Importance and Methods)

By Indeed Editorial Team

Published May 31, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you work with data, you may have large data sets available for statistical studies. Analyzing data sets and reducing them to smaller, comprehensible data can improve work efficiency and make your goals seem more achievable. Learning more about factor analysis can help you when conducting studies on many variables.

In this article, we define factor analysis, explain why it's important, explore the different types of factor analysis, discuss professionals who use it, examine the fields where it's applicable, and provide a list of methods of conducting it.

What is factor analysis?

Factor analysis, or correlational analysis, is a statistical technique that reduces a large number of variables into a few data sets that are more manageable and understandable. This makes it easier to work with research data. It can be an efficient way to simplify complex data sets with many variables.

The variables can form groups of categories, called factors, based on observations. You may determine the similarities and differences between the factors to establish a correlation between them.

Why is correlational analysis important?

A correlational analysis is essential because it can describe a vast data set using fewer dimensions than the original variables. It attempts to discover the unexplained factors that influence how similar variables form co-variations among multiple observations. It provides more details about concepts that a single variable may not accurately measure. This can be helpful, especially in survey research, where the responses to each question represent an outcome.

Another importance of dimension reduction is that it helps uncover clusters of reactions, making analyzing results more accessible and faster. For example, it can help improve customer satisfaction by getting their feedback on products.

What are the types of factor analysis?

The three primary types of correlational analysis are:

Exploratory factor analysis (EFA)

This type of analysis is helpful for data structures with an infinite number of dimensions in a set of variables. The exploratory analysis measures the factors that affect the variables in a data structure without setting any predefined structure to the outcome. EFA's goals is to let the data determine the interrelationships among a set of variables. The analysis is useful in theory development and debates.

Confirmatory analysis

This analysis is helpful for verification where there's a specific idea of the data structure and how many dimensions are available in a set of variables. It's useful as a tool in market research and analysis to confirm the correlation and effect of an existing set of predetermined factors. It can also verify the impact of the variables affecting these factors. The goal of confirmatory analysis is to test theoretically derived hypotheses from a given set of data. It's beneficial in a deductive reasoning process.

Structural equation modelling

This type can be useful for exploratory and confirmatory modelling. It helps confirm analysis results and tests hypotheses. It's also useful for model testing, where you can impose a structure to assess data fit.

Who uses correlational analysis?

Here are a few professionals that use correlational analysis in their work:

Business analysts

Business analysts use this process to derive conclusions from the data of their company or business. They may use different techniques to determine the most accurate factors for a particular data set. This can provide them with conclusions that help a company make effective business decisions about their products, services, and daily operations. It can also provide them with ways to increase customer satisfaction.

Related: What Is a Business Analyst?


Research data can include complex variables in a data set. Researchers use correlational analysis to explain and model the correlations among a set of variables. They use it to simplify data, such as reducing the number of variables in a model. This helps them filter their research data and work with relevant and useful information for their course. Dimension reduction enables researchers to investigate concepts that may not be easy to measure directly.

Related: How To Become a Medical Researcher (And Salary Expectations)


Investing is a field that uses correlational analysis to make vital choices. Investment professionals can use dimension reduction to balance their level of risk. They achieve this by anticipating the movements in various industries and avoiding markets that adverse conditions may likely affect. Investors create portfolios to diversify their stocks. It's a relatively safe approach and can provide new insight for investment opportunities. For instance, if one portfolio manages stocks in two similar industries, it can be safe to assume that investing in a similar industry may generate more profits and returns.

Personality psychologists

Psychologists that study personality focus on the enduring traits and qualities that people demonstrate over time. Correlational analysis helps identify traits by examining people's responses to various situations. Psychologists can generate personality types and behavioural traits from results they get from dimension reduction.

Where is correlational analysis useful?

Correlational analysis can help businesses improve their operations and create the most effective strategies to reach target customers. Here are industries where you might use it:

Automotive industry

Studies can identify important variables in the purchase of an automobile. These variables may include size, price, options, colour, and design. A correlational analysis is beneficial to the automotive industry, as it can condense these variables into a few factors that determine purchasing decisions for customers. Once they identify those factors, the company can tailor their marketing approach to those details.

Insurance companies

These companies rely on data and statistics to create policies. They can make decisions regarding deductibles, rates, and available plans by studying variables. They can consider the best plans for their potential customers based on previous analysis and market them. This can increase the customer base of the company.

Financial institutions

Institutions use correlational analysis to determine their best offers from a set of variables. For instance, if a student is looking to get a loan, you can analyze several factors to determine an institution's best features. Correlational analysis can compile the list of variables and determine the most relevant factors to determine what makes a financial institution a great choice.


Dimension reduction is useful for decision-making processes in education. School data analysis typically determines the curriculum for education from grade school to graduate programs each year. A school council may also consider class sizes and test results to set salary and employee limits for teachers or professors. Other industries that require continuous education can consider different factors to base their decisions.

Methods of conducting correlational analysis

Here are popular methods for extracting factors from a set of data:

Principal component analysis

This analysis identifies fewer uncorrelated variables known as principal components from a more extensive data set. The technique emphasizes variations and detects strong patterns in a data set. This method can predict models and work in fields such as face recognition, image compression, computer graphics, and neuroscience. One distinct advantage of principal component analysis is that it can support data compression once you find patterns in specified data. It's the most common and preferred method that researchers use.

Common correlational analysis

This method is similar to principal component analysis. The only difference is that, while PCA analyzes all the variance of data, common correlational analysis assesses only the reliable common variance of data. It extracts the shared variance with the highest correlation and groups them into factors. This method doesn't include the unique variance of all variables.

Related: What Is Quantitative Analysis?

Image factoring

This method of analyzing factors uses a correlation matrix, which displays the correlation between variables, especially those that demonstrate a linear relationship with each other. Image factoring adopts image theory to give accurate measures of covariance between variables. Image factoring merges human psychology concepts with statistical correlations, and this helps especially in psychological or social studies.

Maximum likelihood method

This method also uses a correlation matrix, relying on the likelihood technique. It identifies factors with the most similarities from the available given data. You can then statistically test them to check for the best fits. It's a valuable technique for analyzing attitude data.

Least-squares method

This method of correlational analysis helps predict the behaviour of dependent variables. The least-squares method determines the line of best fit for a data set and visually represents the relationship between the data points. Each data point represents the relationship between a known dependent variable and an unknown dependent variable.

The different types of least square method in correlational analysis are:

  • Weighted method: involves weighing correlations by the inverse of their uniqueness so that more original variables weigh more. You can consider the weights when determining factors.

  • Unweighted method: The weight of uniqueness of variables isn't relevant in this method. The unweighted method is suitable for situations with many similar observed variables.

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