Factor analysis summarizes a large set of data points into fewer factor groups, making it easier for data analysts to locate patterns. Using this technique allows you to structure your business better, locate key components influencing profits, and allocate resources efficiently.
Our experts at Business2Community compiled this full guide to mastering factor analysis. In this article, we’ll go over the basic parameters of this tool, how to perform this analysis, and real-life examples so you know exactly how to incorporate this technique in your next business planning session.
Factor Analysis – Key Takeaways
- Factor analysis condenses a large amount of variables into fewer factors based on their properties.
- This technique reveals the variance explained by each factor and the correlation between the variable and the outcome.
- Exploratory factor analysis (EFA) and principal component analysis are the two most common types of factor analysis methods used by data analysts.
What Is a Factor Analysis?
Factor analysis is the process of condensing a large set of variables into a few with similar properties. It lets you group observed variables together based on their similarities and characteristics, allowing you to more easily identify patterns and underlying factors that contribute to the outcome you are interested in.
Those smaller groups of observed variables are also known as latent variables or unobserved variables. Variables in the same group share the same variance. With factor analysis, you can get an idea of the number of factors influencing an occurrence in your business and strategize accordingly.
For example, your marketing team wants to study the consumer behavior of a target group with a high socioeconomic status to deliver a better user experience. There are a number of factors related to this topic, such as the ease of purchase, delivery quality, and product satisfaction. These are the factors accounting for their observed behavior.
All these independent variables affect the dependent variable (sales) and can be grouped into a latent variable group, which is “customer satisfaction”. This factor explains the motivation for your customers to purchase from you.
Who Needs to Do a Factor Analysis?
Factor analysis can be a valuable tool for nearly anyone in the business world, especially business owners, analysts, marketers, and decision-makers. If you’re struggling to parse complex data or information of any kind, a factor analysis will likely be your best friend.
Everyone in the ecommerce sector should familiarize themselves with this incredible tool to successfully identify the factor structure and underlying factors that have previously been missed. Factor analysis leads to a more efficient production flow, a better understanding of operations, and more profitable business strategies.
From entrepreneurs to market analysts and stock traders, factor analysis can bring tremendous benefits to your work. Follow this guide so you can utilize this strategy for your business growth.
How to Perform a Factor Analysis
Creating a factor model detailing the number of factors contributing to an outcome plus their specific and error variance allows you to gain a deeper understanding of the crucial latent factors in your operations.
Statistical software packages are great resources to increase efficiency and minimize human mistakes in calculations. You can incorporate digital tools in your research process to generate the most accurate results.
To perform factor analysis, follow these steps.
Step 1: Choose Your Factor Analysis Method
There are two key concepts in the factor analysis methods, which are exploratory factor analysis and confirmatory factor analysis. Each factor model contributes to your research process differently.
Choosing the right factor analysis model to work with can make the process easier to complete and give you reliable results you can work with.
Exploratory Factor Analysis (EFA)
As its name suggests, exploratory factor analysis aims to explore all the latent factors affecting the outcome. It doesn’t make any prediction about the observed variables or the linear combinations among factors.
You’re investigating the data points available and the factor structure without knowing the common variance or total variance. The extracted factors explain the event you’re studying and you can draw your conclusion based on that.
The two most popular techniques used in exploratory factor analysis are principal components analysis, otherwise known as principal component analysis and common factor analysis.
The principal component analysis assumes the total variance to be the same as the common variance. There is no unique variance explained by a particular factor.
Common factor analysis is often the preferred method for researchers because it reflects reality by assuming an imperfect factor extraction. Its extraction method breaks down the total variance to put the common variance into different latent variables.
When you’re trying to explore patterns or locate the underlying factor by examining the observable variables, use exploratory factor analysis.
Confirmatory Factor Analysis (CFA)
Confirmatory factor analysis involves testing a hypothesis. To perform factor analysis, you need to already have some idea about the latent variables, variance explained by the factors, and the total variance.
The purpose of this method is to test if your hypothesis about the observed variables is correct. The chi-squared test, elucidated below, is a technique used to determine how well your hypothesis fits the factor analysis model.
Principal component analysis is another common technique for confirmatory factor analysis. It generates an eigenvalue, which is used to normalize data and shows the variance explained by a particular factor you are studying in the total variance.
Step 2: Gather Your Data
Now that you have figured out which type of factor analysis you want to use, it’s time to start collecting raw data for your study. You can draw data from survey responses, company records, and other reliable channels.
A prudent data collection process ensures that your factor extraction method runs smoothly and keeps the error variance, common variance, and total variance as close to reality as possible.
Step 3: Construct a Hypothesis
Even before you perform factor analysis, you might already have an idea about the number of factors, latent variables, and the correlation matrix.
As a researcher, you can create a hypothesis about linear combinations and other characteristics and use factor analysis to prove or disprove your theories. Setting a hypothesis about your factor matrix can offer guidance in the research process.
However, you should still keep an open mind about the results so you do not get misled by your personal judgment.
Step 4: Select a Factor Analysis Method
The most common factor analysis methods are exploratory factor analysis and confirmatory factor analysis. Choosing the right method draws the most accurate correlation matrix for the observed variables.
If you’re unsure about the factor matrix and its total variance, you can opt for exploratory factor analysis. If you already have a hypothesis about any two factors or other components, you can choose confirmatory factor analysis.
Step 5: Construct Your Correlation Matrix
Once you’ve chosen the appropriate factor analysis method, it’s time to build your correlation matrix. The correlation coefficient demonstrates the relationships between a specific factor and the outcome. Factor loadings represent the correlation, denoted by a value between 1 to -1.
Understanding factor loadings builds the foundation for your correlation matrix. As a general rule of thumb in the factor loading matrix, values beyond 0.7 or -0.7 usually indicate sufficient variance explained by the variables.
Factor loadings can only reveal observed variables that relate to the outcome but can’t calculate how variables interact with each other.
Step 6: Choose an Appropriate Factor Extraction Method
Principal component analysis is a popular factor extraction method that identifies principal components in the factor matrix sharing similar properties. It can handle independent variables that are correlated to the outcome, but not interlinked, based on the factor loading scores.
On the other hand, if you are conducting multivariate behavioral research and believe two factors or more are correlated, you can use principal axis factoring, which is a type of factor analysis that reveals the number of factors sharing the same variance out of the total variance.
Multivariate behavioral research looks into factors that are interlinked and influence the dependent variable together. It’s an important practice for data scientists to construct a comprehensive factor analysis model.
Step 7: Find Out the Number of Factors
Now that you have all the data in place, it’s time to conclude the number of factors. The Kaiser rule states that all principal components with an eigenvalue less than 1 should be eliminated.
However, the execution depends on your principal components’ characteristics and your scope of study. Even if your principal component analysis tells you to remove certain principal components, you should only do so if it makes sense to your research.
Step 8: Interpret and Share the Results with Your Team for Review
Based on the results, you can now locate the underlying factors contributing to the occurrence you’re interested in. Share the factor matrix observations and results with your team to discuss actionable plans to bring up performances.
Examples of Factor Analysis
Now that we’ve covered a step-by-step guide to performing factor analysis and determining the number of factors, we can dive into some real-life applications of one of the best statistical methods to assist your decision-making journey.
With these examples, you’ll learn about utilizing this technique in analyzing your business elements.
Example 1: Use Factor Loadings to Identify Relevant Variables
You’re a marketer trying to study consumer behavior through a survey. You can use factor loadings to find out factors like socioeconomic status and purchase process that influence variables like customer satisfaction.
Positive factor loadings indicate a positive relationship between observed variables and the outcome while negative factor loadings suggest otherwise. The higher the absolute factor loading score, the greater the correlation. You can present the factor loading scores on a table so it is easily readable.
Example 2: Use Principal Component Analysis to Determine Feasible Solutions
You are an owner of a beauty salon and you want to use principal component analysis to locate several solutions to improve sales. This technique in factor analysis assumes the variance explained by each factor to be equal and generates a factor solution with multiple options.
With this tool, you can examine a number of factors that can improve your operations like upgrading your equipment, introducing discount codes, and hiring more shop assistants.
Example 3: Use Factor Analysis to Target Operational Challenges
Factor analysis is a great tool for HR managers to target human resources issues hindering the production flow. It can process large data points like low salaries, outdated workplaces, and poor management into small groups like organization barriers and technical barriers so the HR team can address the problems promptly.
How to Adjust a Factor Analysis
Since factor analysis involves dealing with a large data set and complicated calculations, adjusting the process can be time-consuming but it is nonetheless important.
For example, you have discovered a mistake in your previous principal components analysis or factor loadings. This can ruin the subsequent steps and you may have wrongly identified two factors or the first principal component due to the mistake in the extraction method.
A change in factor analysis often requires lengthy updates to make sure other parts are updated. Despite the complex nature of this statistical method, it remains vital to businesses as it reveals the principal component, the factor score, and other important indicators that can help curate your next business strategy.
Limitations of Factor Analysis
Since factor analysis also simplifies variables into more manageable and readable factors, it is one of the most powerful statistical methods. Working with fewer variables facilitates the data analysis process and sets the foundation for more complex analyses in the future.
However, the process is extremely complicated, especially for small businesses without sufficient funding for statistics experts. Firms need experts to calculate the variance explained, factor scores, and other mathematical equations, which can be costly and time-consuming.
Furthermore, there is no shortcut in determining the correlation between factors before the analysis. If the original data are unrelated, factor analysis cannot produce meaningful results, hindering its effectiveness.
To compensate for its limitations, you are always advised to adopt factor analysis with other analytical tools like qualitative analysis to minimize the risks and produce more reliable results.
The Value of Factor Analysis
Factor analysis is a handy statistical method that organizes observable variables with common variance for data scientists to locate the primary factors influencing an outcome vital to the operations.
By considering fewer factors, companies can process a larger amount of data smoothly. Factor analysis also allows businesses to construct a correlation matrix explaining the relationship among variables. You can gain a deeper understanding of the variance explained by each variable and the factor matrix concerning your operations.
While it can be a challenging process that requires planning, research, and number-crunching, it can give you a clear understanding of what elements of your business affect each other. With this information, you can go on to find valuable ways to improve your business and boost revenue.