What is it about?

Computer programs produce eigenvalues for each factor in an exploratory factor analysis or principal components analysis. But they do not provide confidence intervals for these eigenvalues. This article gives researchers an easy formula for calculating confidence intervals for their eigenvalues.

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Why is it important?

Eigenvalues are sample statistics. Like all sample statistics, they are subject to sampling error. Reporting a confidence interval with each eigenvalue helps researchers understand their exploratory factor analysis (or principal components analysis) data better.


I had a great time working with my friend, classmate, and colleague Ross Larsen on this piece. We've since published two other articles together.

Dr Russell T. Warne
Independent Scholar

Read the Original

This page is a summary of: Estimating confidence intervals for eigenvalues in exploratory factor analysis, Behavior Research Methods, August 2010, Springer Science + Business Media,
DOI: 10.3758/brm.42.3.871.
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