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Principal Components and Factor Analysis

Instructor(s):

Dates:

May 23, 2014 to June 20, 2014

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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 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.

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

Be sure you meet all of the minimum requirements before you register, click here to learn more.

Instructor(s):

Dates:
May 23, 2014 to June 20, 2014

Course Fee: $629

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)


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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.

Principal Components and Factor Analysis

taught by Anthony Babinec

Who Should Take This Course:

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

Level:

Intermediate/advanced

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

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.

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.

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.

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.


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