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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.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.

Course Program:

  • Week 1: Methods
  • Week 2: Choosing the Correct Number of Factors
  • Week 3: Rotation
  • Week 4: Use of Factor Scores

Important Date:

May 22, 2015 to June 19, 2015


4 Weeks

Time Requirement:

About 15 hours per week, at times of  your choosing.


INR 37,740 (assuming $ = INR 60)

Part Time/Full Time:

Part Time

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


  • Some prior work with modeling is helpful.
  • SPSS
  • Anthony Babinec
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