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.

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.

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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.

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.

Instructors

dr-anthony-babinec

Mr. Anthony Babinec

Anthony Babinec is the President of AB Analytics. For over two decades, Tony Babinec has specialized in the application of statistical and data mining methods to the solution of business problems. Before forming AB Analytics, Babinec was Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS users. He is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has held various offices including President.
See Instructor Bio

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

2020

May 15, 2020 to Jun 12, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

Some prior work with modeling is helpful.

Recommended

We recommended, but do not require as eligibility to enroll in this course, an understanding of the material covered in these following courses.

Logistic Regression

This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure. NOTE: Jan. 10 session is still open – register for a later class and email ourcourses@statistics.com to switch.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jul 17, 2020

Predictive Analytics 1 – Machine Learning Tools

This course introduces the basic paradigm of predictive modeling: classification and prediction. NOTE: Jan. 10 session is still open – register for a later class and email ourcourses@statistics.com to switch.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 15, 2020, Sep 11, 2020, Jan 15, 2021

Regression Analysis

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 17, 2020, May 8, 2020, Oct 2, 2020

What Our Students Say​

The material covered here will be indispensable in my work. I can't wait to take other courses. Great work!

Stephen McAllister
Library Planning Consultant at Ottawa Public Library

I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.

Amir Aminimanizani
MRR Pharmacy Consulting, Inc

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Multivariate Statistics

This course will teach you key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Jan 31, 2020, Jan 29, 2021

Additional Course Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

Please order a copy of your course textbook prior to course start date.

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.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
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.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Principal Components and Factor Analysis
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