Factor Analysis

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by Foti Panagiotakopoulos Founder at GrowthMentor

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Factor analysis can be tricky to understand, but don’t let that scare you away. It is a powerful tool with the potential to optimize the way you do business. In other words, it’s well worth the effort to learn!

What Is a Factor?

First, let’s talk about factors.

In analytics, a factor is the combination of variables. Factors can be numeric or categorical. They can also be derived from other factors.

For example, the factor “age” could be created by combining the variables “year of birth” and “current year”. The factor “income” could be created by combining the variables “hourly wage” and “hours worked”.

What Is Factor Analysis? 

Factor analysis is a statistical method that is used to analyze the relationships between many variables at once. To put it another way, factor analysis is a way of testing a data set to see if it’s more structured than it appears on the surface.

Imagine you’re measuring the income (X) and educational attainment (Y) of a set of people. There are two separate variables here (X, Y), but you find through your study that they’re highly correlated. 

People with high incomes often have advanced degrees (and vice versa). In this case, X and Y could be combined into a single factor (i.e., socioeconomic status) to be used in analysis.

Factor analysis can be used to find these hidden relationships in data sets. It’s a way of distilling complex data sets into a smaller number of factors. These factors are created by combining the original variables in a way that optimizes for two things:

  1. The number of factors. Fewer factors is better because it makes the data set more manageable and easier to derive insights from.
  2. The amount of variation explained by each factor. More variation is better because it means that the factors are capturing important relationships between the variables.

How Does Factor Analysis Work?

There are two main types of factor analysis: exploratory factor analysis and confirmatory factor analysis.

1. Explanatory Factor Analysis

Explanatory factor analysis is a way of testing data to see if it is more organized than it seems at first. It can also be used to find hidden relationships in data sets. This type of factor analysis is used to find the number of factors and the amount of variation explained by each factor.

It’s also the kind of factor analysis that’s most commonly used by businesses!

2. Confirmatory Factor Analysis

Confirmatory factor analysis is used to test a hypothesis. In other words, it is used to confirm or deny the existence of a relationship between variables.

Confirmatory factor analysis is more complex than explanatory factor analysis. It requires the use of specialized software (e.g., SPSS) and a deep understanding of statistics.

How to Interpret Factor Analysis Results

The results of a factor analysis can be difficult to interpret.

Here are some things to keep in mind:

  1. Factors are not independent variables. In other words, they cannot be used to predict other variables.
  2. Factors are not necessarily meaningful. They are mathematical constructs that may or may not have any real-world interpretation.
  3. Factor loadings are not always clear. Factor loadings can be difficult to interpret because they are based on correlations between variables. A high factor loading does not necessarily mean that a variable is important.
  4. Factors are not always stable. The factors extracted from a data set can change depending on the method used, the variables included, and the sample of data.

Why Might a Startup Use Factor Analysis?

Startups often use factor analysis to understand customer behavior. By understanding the factors that influence customer behavior, startups can create better products and services.

Startups can also use factor analysis to segment their customers more optimally. This is useful for targeted marketing and product development, because it allows startups to focus their resources on the segments of customers that are most likely to be interested in their products.

Enhance Your Learning

If you are ready to explore factor analysis for your startup or need some assistance using explanatory factor analysis in SPSS, GrowthMentor will be able to put you in contact with a helpful mentor who can assist you with these queries. 

Suggested mentors to help you make sense of Factor Analysis

Michael Taylor

Co-Founder @ Vexpower | Marketing Memeticist | Ex-Founder @ Ladder

Data-driven, technical marketer with 11 years experience, 8,000 experiments run, and $50m optimized across all 4 major growth channels. Author of Marketing Memetics, Co-Founder at Vexpower, Ex-Founder at Ladder.

Kuba Nowak

Data-Driven Growth for 💻 SaaS 🤝 Marketplace 📱 Mobile Apps 🛒 eCommerce 📈 Growth Strategy 🎏 Experimentation Consulting

I’m focused on building strategic and sustainable growth machines where Acquisition, Retention, and Monetization are considered across Marketing & Product, and Experimentation, User psychology, and Data are leveraged in the decision-making process.

Sunni Sukumar

Marketing Director

I come in peace from Planet DorkNerd to help you with your tracking / analytics. We’ll have a plain English convo to measure what’s working, so you can do more of it.

Also an expert in:

Dimitri Visnadi

Data Science & Analytics Consultant

I help companies with their data. I’m a business oriented Data Scientist focusing on Marketing data. And that’s exactly the expertise I want to share with you.

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