Principal Components and Factor Analysis

# Principal Components and Factor Analysistaught 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

## WEEK 2: Choosing the Correct Number of Factors

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

## WEEK 3: Rotation

• Varimax
• Quartimax
• Oblique rotation

## WEEK 4: Use of Factor Scores

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.

# Principal Components and Factor Analysis

Who Should Take This Course:
Market researchers, educational and psychological researchers, sociologists, political scientists, survey researchers.
Level:
Prerequisite:
Some prior work with modeling is also helpful - statistics.com courses that are useful in this respect include Regression, Predictivce Analytics 1, and Logistic Regression.
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.
5. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
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.  For information on software, including free licenses for students, click here.
Instructor(s):

Dates:

May 15, 2020 to June 12, 2020

# Principal Components and Factor Analysis

Instructor(s):

Dates:
May 15, 2020 to June 12, 2020

Course Fee: \$589

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

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