Understanding Factor Analysis in Psychology

<p>Skynesher / Getty Images</p>

Skynesher / Getty Images

Like many methods, those studying psychology use, factor analysis has a long history.

The primary goal of factor analysis is to distill a large data set into a working set of connections or factors.

It was originally developed by British psychologist Charles Spearman in the early 20th century and has gone on to be used in not only psychology but in other fields that often rely on statistical analyses,

But what is it, what are some real-world examples, and what are the different types? In this article, we'll answer all of those questions.

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What Is Factor Analysis and What Does It Do?

The primary goal of factor analysis is to distill a large data set into a working set of connections or factors. Dr. Jessie Borelli, PhD, who works at the University of California-Irvine, uses factor analysis in her work on attachment.

She is doing research that looks into how people perceive relationships and how they connect to one another. She gives the example of providing a hypothetical questionnaire with 100 items on it and using factor analysis to drill deeper into the data.

"So, rather than looking at each individual item on its own I'd rather say, 'Is there is there any way in which these items kind of cluster together or go together so that I can... create units of analysis that are bigger than the individual items."

Factor analysis is looking to identify patterns where it is assumed that there are already connections between areas of the data.

An Example Where Factor Analysis Is Useful

One common example of a factor analysis is when you are taking something not easily quantifiable, like socio-economic status, and using it to group together highly correlated variables like income level and types of jobs.

Factor analysis isn't just used in psychology but also deployed in fields like sociology, business, and technology sector fields like machine learning.

Types of Factor Analysis

There are two types of factor analysis that are most commonly referred to: exploratory factor analysis and confirmatory factor analysis.

Here are the two types of factor analysis:

  1. Exploratory analysis: The goal of this analysis is to find patterns in a set of data points.

  2. Confirmatory factor analysis: The goal of this analysis is to test various hypotheses about a certain set of data points.

Exploratory Analysis

In an exploratory analysis, you are being a little bit more open-minded as a researcher because you are using this type of analysis to provide some clarity in your data set that you haven't yet found. It's an approach that Borelli uses in her own research.

Confirmatory Factor Analysis

On the other hand, if you're using a confirmatory factor analysis you are using the assumptions or theoretical findings you have already identified to drive your statistical model.

Unlike in an exploratory factor analysis, where the relationships between factors and variables are more free flowing, a confirmatory factor analysis requires you to select which variables you are testing for. In Borelli's words:

"When you do a confirmatory factor analysis, you kind of tell you tell your analytic program, what you think the data should look like, in terms of I would say, 'I think it should have these two factors and this is the way I think it should look.'"

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Advantages and Disadvantages of Factor Analysis

Let's take a look at the advantages and disadvantages of factor analysis.


A main advantage of a factor analysis is that it allows researchers to drill deeper into heavily complex data sets while reducing the risk of losing the nuance involved.

When answering your research questions, it's a lot easier to be working with three variables than thirty, for example.


One disadvantage, especially when a factor analysis model for a certain tool is common, is that it can lead researchers to question themselves on multiple fronts when the results they receive are different from what they expected.

For example, during one study, Borelli found that after deploying a factor analysis, she was still left with results that didn't connect well with what had been found in hundreds of other studies.

Due to the nature of the sample being new and being more culturally diverse than others being explored, she used an exploratory factor analysis that left her with more questions than answers.

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How Is Factor Analysis Used in Psychology?

The goal of factor analysis in psychology is often to make connections that allow researchers to quantify what is initially hard or impossible to observe.

So, for example, if we take panic as a factor by itself, that is difficult to quantify. However, if we use a well-established factor analysis model that is used with common diagnostic tools, we can make some solid conclusions about someone's experience of panic.

Factor analysis has often been used in the field of psychology to help us better understand personality and personality disorders.

This is due to the multitude of factors researchers have to consider when it comes to how someone understands themselves. This area of research is certainly not new, with easily findable research dating as far back as 1942 singing its praises.

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