Principal Components and Factor Analysis

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  • 24 May 2013
  • statistics.com
  • Organiser: statistics.com
  • Event Details

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.

Please note this is an online course.

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

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