April 8th, 2026
What is factor analysis? Types, uses, and challenges
By Drew Hahn · 17 min read
What is factor analysis?
Factor analysis is a statistical method that groups a large number of variables into a smaller set of underlying patterns or themes called factors. You use it to find what a collection of data points has in common, without having to measure those shared themes directly.
It works well when you have a lot of variables that seem connected, and you want to figure out what's driving those connections. It won't explain the "why" behind the patterns, but it gives you a cleaner, more focused view of what your data is actually telling you.
A customer survey is a good way to see this in action. Say you send out a 20-question survey covering price, delivery speed, packaging, and support quality. Factor analysis can condense those responses into two or three core themes that drive satisfaction, like "value perception" and "service experience.” You end up with fewer, more meaningful categories to work with.
Note: The correct term is “factor analysis.” You may occasionally see “factoral analysis” online, but this is typically just a misspelling of factor analysis, and both are intended to refer to the same technique.
2 Types of factor analysis
Factor analysis splits into two main types, and knowing which one fits your situation saves you a lot of time upfront.
Exploratory factor analysis (EFA) is what you'd use when you're starting from scratch. You have a dataset with a bunch of variables and no strong theory yet about how they relate to each other. EFA lets you discover those relationships by grouping variables into factors based on what the data actually shows. It's a good fit early in the research process, when you're still figuring out what patterns exist.
Confirmatory factor analysis (CFA) works the opposite way. You start with a hypothesis about how your variables should group together, then test whether your data backs that up. It's a better fit when you already have a theory or a prior study to build on and want to verify whether it holds in your specific context.
If you're new to factor analysis and working with a new dataset or survey, I'd suggest starting with EFA. It lets the data show you its structure before you commit to any assumptions.
How factor analysis works
Factor analysis works by taking a large set of variables and finding the patterns that connect them. These patterns are called factors, and the process of finding them is called factor extraction.
There are a few ways to do this, but you don’t need to learn all of them to use factor analysis. In my experience, most business users only use one or two. Most tools handle the math for you, so you’re mainly focused on interpreting the results.
The three most common extraction methods are:
Principal component analysis (PCA): The most widely used extraction method. PCA finds the group of variables that explains the most variation in your data and uses it as the first factor. It repeats this process until the eigenvalues, which show how important each factor is, become too small to keep.
Common factor analysis: This method looks at what your variables have in common and ignores what is unique to each one. Each variable’s connection to a factor is shown by its factor loading, where a higher number means a stronger connection.
Image factoring: This method uses the relationships between variables to calculate each one, then extracts factors from the patterns those relationships reveal. It’s less common than the first two, but it can be useful when your variables are closely linked. The resulting factor scores give you a summary number for each variable that you can use in further analysis.
6 assumptions your data should meet for factor analysis
In statistics, assumptions are the conditions your data needs to meet for an analysis to produce reliable results. Factor analysis has six of them. If your data doesn’t meet these conditions, your results may be misleading, so I’d check this before running the analysis.
Factor analysis works best when your data meets these conditions:
No outliers: Extreme values in your data can skew your factors and pull your results in the wrong direction. Clean your data before running the analysis.
Adequate sample size: Your data needs enough data points to produce stable, meaningful factors. A larger sample generally gives you more reliable results.
No duplicate variables: Your variables can be related, but none of them should be a perfect copy of another. If two variables measure exactly the same thing, the analysis can’t separate them into different factors.
Equal variance not required: Unlike some other statistical methods, factor analysis doesn't require your variables to have the same spread of values, so this is one condition you don’t need to worry about.
Linearity: Factor analysis assumes the relationships between your variables are linear. Non-linear variables can be used, but you need to adjust them first.
Interval data: Your data should be measured on a numeric scale, such as ratings, scores, or counts, rather than categories like yes or no.
When to use factor analysis
Factor analysis is worth using when you have a large number of variables and need a cleaner way to understand what they have in common. The goal is always the same: reduce complexity without losing the meaning behind your data.
There are a few objectives it helps you work toward. You can use it to:
Figure out how many underlying themes exist across a set of variables
Measure how strongly each variable connects to those themes
Create a simplified set of factors you can carry into further analysis.
For a business user, that might mean turning 30 survey questions into 4 or 5 clearer, more useful categories, or finding the key drivers behind a metric that's influenced by dozens of different inputs.
I've seen this come up most often in marketing and customer research, where teams are sitting on a lot of survey data but struggling to draw clear conclusions from it. Factor analysis gives you a way to cut through that and focus on what the data is actually pointing to.
When to use factor analysis
Marketing
Marketing teams use factor analysis to make sense of customer survey data. Say you run a brand perception survey with 25 questions covering trust, price, quality, and convenience. Factor analysis can group those responses into a handful of core themes that drive purchase decisions. This means you’re not trying to act on 25 separate data points at once.
I find it useful for teams that run regular customer research but struggle to turn the results into clear action items.
Finance
Human resources
HR teams use factor analysis to understand what drives employee satisfaction, engagement, or performance. If you run an annual employee survey with dozens of questions, factor analysis can condense those responses into a smaller set of themes, like management quality or work-life balance. This gives you a clearer picture of where to focus your efforts.
Product
Product teams use factor analysis to check whether their surveys and feedback forms are measuring what they’re supposed to measure. If you're tracking feature satisfaction across 20 different questions, factor analysis can tell you whether those questions are capturing distinct experiences or whether several of them are essentially measuring the same thing.
Market research
Challenges of factor analysis
Factor analysis is a useful technique, but it comes with a few hurdles worth knowing about before you dive in. Here are five challenges that come up often:
Interpreting results without a stats background: Factor analysis produces outputs like eigenvalues and factor loadings that can be hard to understand if you’re not a statistician. Understanding what these numbers mean and how to turn them into business decisions takes practice.
Choosing the right variables: The quality of your factors depends heavily on the variables you put in. If you leave out an important variable or include ones that aren’t relevant, your factors won’t reflect the full picture. I've seen this trip up a lot of teams that rush the data selection step.
Data quality and cleaning: Factor analysis is sensitive to messy data. Duplicate variables, missing values, and outliers can all skew your results. This happens before the analysis even begins. Getting your data into good shape beforehand saves a lot of headaches later.
Knowing how many factors to extract: There's no single rule for deciding how many factors to keep. Extract too many, and your results get noisy; extract too few, and you lose important patterns. It often takes a few rounds of testing to land on the right number.
Naming and interpreting your factors: Once you've extracted your factors, you still need to figure out what they actually mean. Factors don’t come with labels, so you have to interpret them based on which variables are most strongly linked to each one. This is where domain knowledge really matters.
Want to run factor analysis without the complexity? Try Julius
Running factor analysis on large datasets can be time-consuming and technically demanding, especially without a stats background. With Julius, you can upload your data and start finding patterns by asking questions in everyday language.
Julius is an AI-powered data analysis tool that connects to your existing data sources or finds and compiles public datasets on your behalf. It turns your data into insights, charts, and reports without requiring you to write a single line of code.
Here’s how Julius helps:
Data search: Type your question, and Julius can search for relevant public data or pull live financial market data for over 17,000 companies through its Financial Datasets integration, so you don’t need a file or database connection to begin.
Direct connections: Link databases like PostgreSQL, Snowflake, and BigQuery, or integrate with Google Ads and other business tools. You can also upload CSV or Excel files. Your analysis can reflect live data, so you’re less likely to rely on outdated spreadsheets.
Repeatable Notebooks: Save an analysis as a notebook and run it again with fresh data whenever you need. You can also schedule notebooks to send updated results to email or Slack.
Smarter over time: Julius includes a Learning Sub Agent, an AI that adapts to your database structure over time. It learns table relationships and column meanings as you work with your data, which can help improve result accuracy.
On-the-fly data cleaning: Remove duplicates, standardize date formats, fill or flag missing values, rename columns, and reshape tables by describing the change you need. Julius runs the transformations in the background, so you don’t have to manually write SQL or build nested spreadsheet formulas to fix messy exports.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
One-click sharing: Turn an analysis into a PDF report you can share without extra formatting.
Ready to see how Julius can help your team make better decisions? Try Julius for free today.
