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Principal Components and Factor Analysis

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.


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

Course Syllabus

Week 1


  • 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


  • Varimax
  • Quartimax
  • Oblique rotation

Week 4

Use of Factor Scores

  • Use of Factor Scores will be discussed

Class Dates


06/14/2024 to 07/12/2024
Instructors: Mr. Anthony Babinec


06/13/2025 to 07/11/2025
Instructors: Mr. Anthony Babinec


Some prior work with modeling is helpful.

Predictive Analytics 1 – Machine Learning Tools

This online course introduces the basic paradigm of predictive modeling: classification and prediction.
  • Skill: Intermediate, Advanced
  • Credit Options: CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Principal Components and Factor Analysis

Additional Information


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.


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

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Principal Components and Factor Analysis