Principal Components and Factor Analysis
In this course, you will learn how to make decisions in building a factor analysis model – including what model to use, the number of factors to retain, and the rotation method to use.
Overview
Exploratory factor analysis (EFA) is a method of identifying the number and nature of latent variables that explain the variation and covariation in a set of measured variables. In this course you will learn how to make decisions in building an EFA model – including what model to use. You will also learn why principal components analysis (PCA) as a method of factoring can serve different goals. Some prior knowledge of modeling will be helpful.
- Intermediate, Advanced
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
This course covers the theory of Exploratory Factor Analysis (EFA) and Principal Components and Factor Analysis (PCA). At the conclusion of the course students will understand the differences between EFA and PCA and will be able to specify different forms of factor extraction and rotation.
- Decide which model to use in a given situation
- Decide how many factors to retain
- Decide which rotation method to use
- Describe the difference between exploratory factor analysis and principal components analysis
- Apply EFA and PCA using software
Who Should Take This Course
Market researchers, educational and psychological researchers, sociologists, political scientists, survey researchers.
Our Instructors
Mr. Anthony Babinec
Anthony Babinec is President of AB Analytics, online data mining instructor at Statistics.com. Specializes in application of statistical and data mining methods in solving business problems.
Course Syllabus
Week 1
Methods
- Principal Components Analysis
- Principal Axes Factor Analysis
- Maximum Likelihood Factor Analysis
Week 2
Choosing the Correct Number of Factors
- Screen plot
- Parallel analysis
- Retaining factors with ML factor analysis
Week 3
Rotation
- Varimax
- Quartimax
- Oblique rotation
Week 4
Use of Factor Scores
- Use of Factor Scores will be discussed
Class Dates
2025
Instructors: Mr. Anthony Babinec
Prerequisites
Some prior work with modeling is helpful.
Predictive Analytics 1 – Machine Learning Tools
- Skill: Intermediate, Advanced
- Credit Options: CEU
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Additional Information
Homework
Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.
In addition to assigned readings, this course also has a discussion forum, the instructor’s expert write-ups on important concepts, and an end of course data modeling project.
Course Text
The course text is Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research by Marjorie A. Pett, Nancy M. Lackey, and John J. Sullivan.
Software
This is a hands-on course and software capable of doing principal components and factor analysis is required; most major general purpose statistical software (SAS, SPSS, Stata, etc.) can do this. The instructor is familiar with SPSS and XLStat.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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