Principal Components and Factor Analysis

taught by Tony Babinec

Brief Description:

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.

Instructor(s):
Level: Intermediate/advanced

Who Should Take This Course:

Market researchers, educational and psychological researchers, sociologists, political scientists, survey researchers.

Dates:
May 25, 2012 to June 22, 2012May 24, 2013 to June 21, 2013
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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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. Multiple course registrations may be entitled to tuition discounts; read more.

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Principal Components and Factor Analysis

taught by Tony 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 course, 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.

Prerequisite(s):

Some prior work with modeling is also helpful - statistics.com courses that are useful in this respect include Regression, Introduction to Data Mining, and Logistic Regression.

Course Program:

SESSION 1: Methods

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

SESSION 2: Choosing the Correct Number of Factors

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

SESSION 3: Rotation

• Varimax
• Quartimax
• Oblique rotation

SESSION 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.

Organization of the Course:

This course takes place over the internet 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.

The course typically requires 15 hours per week. 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.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. 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. 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, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

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, which you can order from Sage.

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

The course will provide illustrations in IBM SPSS Statistics 18 (formerly known as PASW Statistics 18 and also as SPSS). Students are welcome to use other suitable software, although IBM SPSS Statistics GradPac 18 is recommended. The instructor will not be able to provide individualized software instructional support for software other than IBM SPSS Statistics. Click here for software options.

Register Now

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Principal Components and Factor Analysis

taught by Tony Babinec

Instructor(s):
Dates:
May 25, 2012 to June 22, 2012May 24, 2013 to June 21, 2013
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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