Factor analysis is a term used to refer to a set of statistical procedures designed to determine the number of distinct unobservable constructs needed to account for the pattern of correlations among a set of measures. These unobservable constructs that explain the pattern of correlations among measures are referred to as common factors. This article shall look at a better understanding of factor analysis in psychology.
The primary goal of factor analysis is to distill a large data set into a working set of connections or factors.
It was originally discussed by British psychologist Charles Spearman1 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.
Table of Contents
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, Ph.D., 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 is 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.
Types of Factor Analysis
Here are the two types of factor analysis:
- Exploratory analysis: The goal of this analysis is to find general patterns in a set of data points.2
- Confirmatory factor analysis: The goal of this analysis is to test various hypothesized relationships among certain variables.
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 an exploratory factor analysis, where the relationships between factors and variables are more open, 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 your analytic program what you think the data should look like, in terms of, ‘I think it should have these two factors and this is the way I think it should look.'”
Advantages and Disadvantages of Factor Analysis
Let’s take a look at the advantages and disadvantages of factor analysis.
Advantages
A main advantage of a factor analysis is that it allows researchers to reduce the number of variables by combining them into a single factor.
You Can Analyze Fewer Data Points
When answering your research questions, it’s a lot easier to be working with three variables than thirty, for example.
Disadvantages
Disadvantages include that the factor analysis relies on the quality of the data, and also may allow for different interpretations of the data.
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.
How Is Factor Analysis Used in Psychology?
The goal of factor analysis in psychology is often to make connections that allow researchers to develop models with common factors in ways that might be hard or impossible to observe otherwise.
So, for example, intelligence is a difficult concept to directly observe. However, it can be inferred from factors that we can directly measure on specific tests.
Factor analysis has often been used in the field of psychology to help us better understand the structure of personality.
This is due to the multitude of factors researchers have to consider when it comes to understanding the concept of personality. This area of personality research is certainly not new, with easily findable research dating as far back as 1942 recognizing its power in personality research.
I hope you find this article helpful as well as interesting.
