An SAS/IML procedure for maximum likelihood factor analysis.
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.
Chen
Rusan
R
Center for New Designs in Learning and Scholarship, Georgetown University, Washington, DC 20057, USA. chenrs@georgetown.edu
D000465:Algorithms; D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D016013:Likelihood Functions; D008432:Mathematical Computing; D008960:Models, Psychological; D015233:Models, Statistical; D009010:Monte Carlo Method; D011589:Psychology, Experimental / Q000706:statistics & numerical data*; D012984:Software