Exploratory Factor Analysis

Member Training: Exploratory Factor Analysis

March 1st, 2013 by

Many variables we want to measure just can’t be directly measured with a single variable. Instead you have to combine a set of variables into a single index.

But how do you determine which variables to combine and how best to combine them?

Exploratory Factor Analysis.

EFA is a method for finding a measurement for one or more unmeasurable (latent) variables from a set of related observed variables. It is especially useful for scale construction.

In this webinar, you will learn through three examples an overview of EFA, including:

  • The five steps to conducting an EFA
  • Key concepts like rotation
  • Factor scores
  • The importance of interpretability

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

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About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

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Factor Analysis: A Short Introduction, Part 3-The Difference Between Confirmatory and Exploratory Factor Analysis

November 2nd, 2012 by

by Maike Rahn, PhD

An important question that the consultants at The Analysis Factor are frequently asked is:

What is the difference between a confirmatory and an exploratory factor analysis?

A confirmatory factor analysis assumes that you enter the factor analysis with a firm idea about the number of factors you will encounter, and about which variables will most likely load onto each factor.

Your expectations are usually based on published findings of a factor analysis.

An example is a fatigue scale that has previously been validated. You would like to make sure that the variables in your sample load onto the factors the same way they did in the original research.

In other words, you have very clear expectations about what you will find in your own sample. This means that (more…)