Factor+Analysis

Factor Analysis (Multivariate Methods) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Nomenclature | Concepts

"**Factor analysis** is a statistical method used to describe variability among observed variables in terms of fewer unobserved variables called **factors**. The observed variables are modeled as linear combinations of the factors, plus "error" terms. The information gained about the interdependencies can be used later to reduce the set of variables in a dataset."

"Factor analysis is related to principal component analysis (PCA) but not identical. Because PCA performs a variance-maximizing rotation of the variable space, it takes into account all variability in the variables. In contrast, factor analysis estimates how much of the variability is due to common factors ("communality"). The two methods become essentially equivalent if the error terms in the factor analysis model (the variability not explained by common factors, see below) can be assumed to all have the same variance." - [|Wikipedia]

Lattin differentiates between principal components analysis and factor analysis. "Although both methods are used to accomplish the same ends" ...", the underlying models are different." - Lattin

Exploratory Factor Analysis ([|Wikiversity])

- Assumptons ([|Wikiversity])

- Criteria for selecting items ([|Wikiversity])


 * Sources:**
 * Factor analysis. (2009, November 15). In //Wikipedia, The Free Encyclopedia//. Retrieved 20:10, December 1, 2009, from []
 * Exploratory factor analysis. (2009, November 6). In Wikiveristy. Retreived 15:21, December 1, 2009 from []
 * Exploratory factor analysis. (2009, November 6). In Wikiveristy. Retreived 15:21, December 1, 2009 from []
 * Analyzing Multivariate Data, by James Lattin, Douglas Carroll and Green ([|Amazon]), page 11
 * EMSE 271, Fall 2009