This workshop is presented by Dr. Rick DeShon. Exploratory factor analysis represents a set of multivariate techniques used to examine large correlation and covariance matrices. The techniques are typically used to reduce the dimensionality present in a large set of multivariate data (e.g., image filtering) or to identify the structure underlying a set of variables (e.g., psychometric and scale development).

This workshop will cover both common factor and principle component approaches to modeling covariance matrices. Methods for determining the number of factors (latent variables) that best represent the underlying structure of a covariance matrix will be described.

Common rotation methods used to improve the interpretability of the underlying factor structure will be presented and guidelines for use will be described. Methods for computing scores on the latent factors will be reviewed.

After describing the modeling issues, a hands-on exercise using human response data will be used to demonstrate the inferences that may be supported using this set of techniques.

Location:	106 Farrall Agricultural Engineering Hall [map]
Price:	Free to Graduate Students, Faculty and Research Staff
Sponsor:	public
Contact:	Dr. Brian Silver bsilver@msu.edu 517-353-9288
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