Exploratory Factor Analysis - Online Course
A 4-Week On-Demand Seminar Taught by
Kristopher PreacherMonday, May, 5 —
Monday, June 2, 2025
Each Monday you will receive an email with instructions for the following week.
All course materials are available 24 hours a day. Materials will be accessible for an additional 2 weeks after the official close on June 2.
This seminar covers primarily Exploratory Factor Analysis (EFA), which is used extensively in psychology, education, medicine, and management to identify underlying factors or dimensions that explain the variability in a set of observed variables. EFA can be a powerful tool for researchers, providing a number of benefits.
First, EFA allows researchers to represent the observed variables with a smaller number of underlying factors or dimensions. In other words, EFA can help researchers to identify the underlying structure of complex data. This can be helpful when dealing with large data sets, as it simplifies the analysis and can help identify key underlying relationships among the variables. This also can be useful for developing and testing theories and models that explain the behavior of the variables.
Second, EFA can help researchers to identify new variables that may be related to the factors or dimensions identified by the analysis. This can lead to new hypotheses and research questions, as well as new insights into the relationships among variables.
Third, EFA can be used to evaluate the reliability and validity of measurement scales. By identifying the key factors that underlie a set of measurement items, researchers can assess whether the items are measuring the same construct, how well they do so, and whether the scale is correlated in expected ways with other variables.
The seminar covers the theory behind factor analysis, hands-on application to data, exposure to uses of factor analysis in the applied literature, and instruction in popular, freely available EFA software. Key topics include model specification, model fit and evaluation, factor rotation methods, multiple-item instrument and questionnaire development, and sample size and power issues.
The course takes place online in a series of four weekly installments of videos, readings, and exercises, and requires about 6-8 hours/week. You may participate at your own convenience; there are no set times when you are required to be online.
This four-week course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of several modules, which contain videos of the 3-day livestream version of the course in its entirety. There are also weekly exercises that ask you to apply what you’ve learned.
There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Preacher.
More details about the course content
The objective of this seminar is to obtain (a) a firm grounding in the statistical theory of exploratory factor analysis as it is employed in the social and behavioral sciences, and (b) practical experience factor analyzing data to better understand its underlying structure. More specifically, you can expect to:
-
- Gain an understanding of the central statistical concepts underlying the methods (e.g., parameter estimation, factor rotation, goodness of fit).
- Learn a variety of factor analytic techniques (e.g., principal axis and maximum likelihood factor extraction, estimating the number of factors, scale construction).
- Gain experience conducting factor analyses with R, interpreting results, and drawing meaningful substantive conclusions.
In other words, you will become an educated consumer and producer of research involving (or about) factor analysis.
Two books recommended as companion pieces (not required, but highly recommended):
-
- Fabrigar & Wegener (2011). Exploratory Factor Analysis.
- Watkins (2020). A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio.
The objective of this seminar is to obtain (a) a firm grounding in the statistical theory of exploratory factor analysis as it is employed in the social and behavioral sciences, and (b) practical experience factor analyzing data to better understand its underlying structure. More specifically, you can expect to:
-
- Gain an understanding of the central statistical concepts underlying the methods (e.g., parameter estimation, factor rotation, goodness of fit).
- Learn a variety of factor analytic techniques (e.g., principal axis and maximum likelihood factor extraction, estimating the number of factors, scale construction).
- Gain experience conducting factor analyses with R, interpreting results, and drawing meaningful substantive conclusions.
In other words, you will become an educated consumer and producer of research involving (or about) factor analysis.
Two books recommended as companion pieces (not required, but highly recommended):
-
- Fabrigar & Wegener (2011). Exploratory Factor Analysis.
- Watkins (2020). A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio.
Computing
This seminar will use R for examples and exercises. To follow along with applied examples, you are encouraged to have a computer with the latest versions of R and RStudio installed. Both are freely available.
We will mainly use the R packages psych and EFAutilities, but we may use others as the need arises. In addition to psych and EFAutilities, specific R packages that may be used include factoextra, FactoMineR, faoutlier, GPArotation, lavaan, MASS, MBESS, nFactors, and readxl.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
This seminar will use R for examples and exercises. To follow along with applied examples, you are encouraged to have a computer with the latest versions of R and RStudio installed. Both are freely available.
We will mainly use the R packages psych and EFAutilities, but we may use others as the need arises. In addition to psych and EFAutilities, specific R packages that may be used include factoextra, FactoMineR, faoutlier, GPArotation, lavaan, MASS, MBESS, nFactors, and readxl.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
This course will be helpful for researchers in many fields—including psychology, sociology, education, business, political science, public health, and communications, among others—who want to learn how to use exploratory factor analysis to understand the relations among multiple variables in terms of a smaller number of underlying latent constructs. Factor analysis is especially useful for testing hypotheses about the underlying structure of multiple-item measurement instruments, and for evaluating or creating instruments to assess target constructs.
Prerequisite knowledge:
-
- Basic familiarity with R (such as from Introduction to R for Data Analysis, R for SPSS Users, or R for Stata Users).
- Basic familiarity with matrix algebra (materials provided prior to workshop).
This course will be helpful for researchers in many fields—including psychology, sociology, education, business, political science, public health, and communications, among others—who want to learn how to use exploratory factor analysis to understand the relations among multiple variables in terms of a smaller number of underlying latent constructs. Factor analysis is especially useful for testing hypotheses about the underlying structure of multiple-item measurement instruments, and for evaluating or creating instruments to assess target constructs.
Prerequisite knowledge:
-
- Basic familiarity with R (such as from Introduction to R for Data Analysis, R for SPSS Users, or R for Stata Users).
- Basic familiarity with matrix algebra (materials provided prior to workshop).
Registration instructions
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Exploratory Factor Analysis” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Exploratory Factor Analysis. When the course begins on May 5, you can click the play button to get started.
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Exploratory Factor Analysis” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Exploratory Factor Analysis. When the course begins on May 5, you can click the play button to get started.