Factor Analysis 101: The Basics

What is Factor Analysis?

Factor analysis is a powerful data reduction technique that enables researchers to investigate concepts that cannot easily be measured directly. By boiling down a large number of variables into a handful of comprehensible underlying factors, factor analysis results in easy-to-understand, actionable data.

By applying this method to your research, you can spot trends faster and see themes throughout your datasets, enabling you to learn what the data points have in common.

Unlike statistical methods such as regression analysis, factor analysis does not require defined variables.

This statistical method is most commonly used to identify the relationship between all of the variables included in a given dataset.

The Objectives of Factor Analysis

Think of factor analysis as shrink wrap. When applied to a large amount of data, it compresses the original set into a smaller set that is far more manageable, and easier to understand.

This overall objective can be broken down into four smaller objectives:

  1. To definitively understand how many factors are needed to explain common themes amongst a given set of variables.
  2. To determine the extent to which each variable in the dataset is associated with a common theme or factor.
  3. To provide an interpretation of the common factors in the dataset.
  4. To determine the degree to which each observed data point represents each theme or factor.

When to Use Factor Analysis

Determining when to use particular statistical methods to get the most insight out of your data can be tricky.

When considering factor analysis, have your goal top-of-mind.

There are three main forms of factor analysis. If your goal aligns to any of these forms, then you should choose factor analysis as your statistical method of choice:

    1. Exploratory Factor Analysis should be used when you need to develop a hypothesis about a relationship between variables.
    2. Confirmatory Factor Analysis should be used to test a hypothesis about the relationship between variables.
    3. Construct Validity should be used to test the degree to which your survey actually measures what it is intended to measure.

How To Ensure Your Survey is Optimized for Factor Analysis

If you know that you’ll want to perform a factor analysis on response data from a survey, there are a few things you can do ahead of time to ensure that your analysis will be straightforward, informative, and actionable.

Identify and Target Enough Respondents

Large datasets are the lifeblood of factor analysis. You’ll need large groups of survey respondents, often found through panel services, to yield significant and actionable results.

While variables such as population size and your topic of interest will influence how many respondents you need, it’s best to maintain a “more respondents the better” mindset.

The More Questions, The Better

While designing your survey, load in as many specific questions as possible. Your analysis will fall flat if your survey only has a few broad questions.  

The ultimate goal of factor analysis is to take a broad concept and simplify it by considering more granular, contextual information, so this approach will provide you the results you’re looking for.

Aim for Quantitative Data

If you’re looking to perform a factor analysis, you’ll want to avoid having open-ended survey questions.

By providing answer options in the form of scales (whether they be Likert Scales, numerical scales, or even ‘yes/no’ scales) you’ll save yourself a world of trouble when you begin conducting your factor analysis. Just make sure that you’re using the same scaled answer options as often as possible.


Have you used this statistical method to help you or your business make informed decisions? If so, we’d love to hear what you thought of the process.

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