An SAS/IML procedure for maximum likelihood factor analysis

5Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Maximum likelihood factor analysis (MLFA), originally introduced by Lawley (1940), is based on a firm mathematical foundation that allows hypothesis testing when normality is assumed with large sample sizes. MLFA has gained in popularity since Jöreskog (1967) implemented an iterative algorithm to estimate parameters. This article presents a concise program using matrix language SAS/IML with the optimization subroutine NLPQN to obtain MLFA solutions. The program is pedagogically useful because it shows the step-by-step computational processes for MLFA, whereas almost all other statistical packages for MLFA are in "black boxes." It is also demonstrated that this approach can be extended to other multivariate methods requiring numerical optimizations, such as the widely used structural equation modeling. Researchers may find this program useful in conducting Monte Carlo simulation studies to investigate the properties of multivariate methods that involve numerical optimizations.

Cite

CITATION STYLE

APA

Chen, R. (2003). An SAS/IML procedure for maximum likelihood factor analysis. Behavior Research Methods, Instruments, and Computers, 35(2), 310–317. https://doi.org/10.3758/BF03202557

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free