Principal Components and Factor Analysis

Principal Components and Factor Analysis

taught by Anthony Babinec

Aim of Course:

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 online course, "Principal Components and Factor Analysis" you will learn how to make decisions in building an EFA model - including what model to use, the number of factors to retain, and the rotation method to use. Because of similarities in the underlying mathematics, factor analysis routines often offer principal components analysis (PCA) as a method of "factoring", yet EFA and PCA have different models and serve different goals. This course covers the theory of EFA and PCA, and features practical work with computer software and data examples. 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.

This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

WEEK 1: Methods

  • Principal Components Analysis
  • Principal Axes Factor Analysis
  • Maximum Likelihood Factor Analysis

Choosing the Correct Number of Factors

  • Scree plot
  • Parallel analysis
  • Retaining factors with ML factor analysis


  • Varimax
  • Quartimax
  • Oblique rotation

Use of Factor Scores


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.

Principal Components and Factor Analysis

Who Should Take This Course:
Market researchers, educational and psychological researchers, sociologists, political scientists, survey researchers.
Some prior work with modeling is also helpful - courses that are useful in this respect include Regression, Predictivce Analytics 1, and Logistic Regression.
Organization of the Course:
Options for Credit and Recognition:
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.  For information on software, including free licenses for students, click here.


May 17, 2019 to June 14, 2019 May 15, 2020 to June 12, 2020

Principal Components and Factor Analysis


May 17, 2019 to June 14, 2019 May 15, 2020 to June 12, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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