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Simultaneous parameter estimation in exploratory factor analysis: an expository review (2010).
| Content Provider | CiteSeerX |
|---|---|
| Author | Unkel, Steffen Trendafilov, Nickolay T. |
| Abstract | The classical exploratory factor analysis (EFA) finds estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample correlation matrix with respect to some goodness-of-fit criterion. Common factor scores can be obtained as a function of these estimates and the data. Alternatively to the classical EFA, the EFA model can be fitted directly to the data which yields factor loadings and common factor scores simultaneously. Recently, new algorithms were introduced for the simultaneous least squares estimation of all EFA model unknowns. The new methods are based on the numerical procedure for singular value decomposition of matrices and work equally well when the number of variables exceeds the number of observations. This paper provides an account that is intended as an expository review of methods for simultaneous parameter estimation in EFA. The methods are illustrated on Harman’s five socio-economic variables data and a high-dimensional data set from genome research. |
| File Format | |
| Publisher Date | 2010-01-01 |
| Access Restriction | Open |
| Subject Keyword | Simultaneous Parameter Estimation Expository Review Exploratory Factor Analysis New Algorithm Common Factor Score Efa Model Unknown Classical Exploratory Factor Analysis New Method Socio-economic Variable Data Classical Efa Numerical Procedure Sample Correlation Matrix Singular Value Decomposition High-dimensional Data Set Square Estimation Efa Model Common Factor Genome Research Unique Factor Variance Goodness-of-fit Criterion Yield Factor Loading |
| Content Type | Text |
| Resource Type | Article |
