Factor analysis of variables with 2, 3, 5 and 7 response categories: A comparison of categorical variable estimators using simulated data
Article first published online: 4 AUG 2011
1994 The British Psychological Society
British Journal of Mathematical and Statistical Psychology
Volume 47, Issue 2, pages 309–326, November 1994
How to Cite
Dolan, C. V. (1994), Factor analysis of variables with 2, 3, 5 and 7 response categories: A comparison of categorical variable estimators using simulated data. British Journal of Mathematical and Statistical Psychology, 47: 309–326. doi: 10.1111/j.2044-8317.1994.tb01039.x
- Issue published online: 4 AUG 2011
- Article first published online: 4 AUG 2011
- Received 20 November 1992, Revised version received 23 September 1993
- Cited By
Two estimators in the factor analysis of categorical items are studied, the weighted least squares function implemented in the tandem PRELIS-LISREL 7 and a generalized least squares function implemented in LISCOMP. Of main interest is the performance of these estimators in relatively small samples (200 to 400) and the comparison of their performance with the normal theory maximum likelihood estimator given an increasing number of response categories. The evaluation of the performance of these estimators concerns the variability of the parameter estimates, the bias of the parameter estimates, the distribution of the parameter estimates and the χ2 goodness-of-fit statistics. The model used in the simulation is an 8-indicator single common factor model. The effect of model size (12- and 16-indicator models) on the categorical item estimator of LISREL 7 is investigated briefly.
The results indicate that in the ideal circumstances of the simulation study, 200 is too small a sample size to justify the use of large sample statistics associated with these estimators.