Starting out with a unidimensional EFA

Let's begin by using the psych package and conducting a single-factor explanatory factor analysis (EFA). The fa() function conducts an EFA on your data. When you're using this in the real world, be sure to use a dataset that only contains item responses - other types of data will cause errors and/or incorrect results. In the gcbs dataset, these are examinees' responses to 15 items from the Generic Conspiracist Beliefs Scale, which is designed to measure conspiracist beliefs.

An EFA provides information on each item's relationship to a single factor hypothesized to be represented by each of the items. EFA results give you basic information about how well items relate to that hypothesized construct.

Be sure to save the analysis result object so you can return to it later.

This exercise is part of the course

Factor Analysis in R

View Course

Exercise instructions

Load the psych package to gain access to the necessary functions for your exploratory factor analysis.
Then, run a single-factor EFA on the gcbs dataset and save the result to an object named EFA_model.
Finally, call the EFA_model object to see how the items in the dataset relate to the extracted factor.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load the psych package
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# Conduct a single-factor EFA
EFA_model <- ___(gcbs)

# View the results
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Edit and Run Code