Exploratory Factor Analysis - Online Course
A 4-Day Livestream Seminar Taught by
Kristopher Preacher10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
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.
Starting June 26, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
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 (now Posit) 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 (now Posit) 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.
- 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.
- Basic familiarity with matrix algebra (materials provided prior to workshop).
Seminar outline
Day 1
-
- Introduction: Aims & overview of factor analysis
- History and conceptual overview
- Understanding the common factor model
- Fitting the common factor model to data
Day 2
-
- Fitting the common factor model to data (cont’d)
- Model identification and parameter estimation
- Computer programs and illustrations
- Estimating the number of common factors
Day 3
-
- Factor rotation: Orthogonal and oblique methods
- Principal components analysis
- Factor scores
- Power and sample size
Day 4
-
- Factor analysis with categorical data
- Using factor analysis for scale construction
- Multilevel factor analysis
Day 1
-
- Introduction: Aims & overview of factor analysis
- History and conceptual overview
- Understanding the common factor model
- Fitting the common factor model to data
Day 2
-
- Fitting the common factor model to data (cont’d)
- Model identification and parameter estimation
- Computer programs and illustrations
- Estimating the number of common factors
Day 3
-
- Factor rotation: Orthogonal and oblique methods
- Principal components analysis
- Factor scores
- Power and sample size
Day 4
-
- Factor analysis with categorical data
- Using factor analysis for scale construction
- Multilevel factor analysis
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.