A comparison of maximum likelihood and expected a posteriori estimation for polychoric correlation using Monte Carlo simulation

Journal of Modern Applied Statistical Methods

Volumn 8, Issue 1, 2009, Pages 337-354

  Chen, Jinsong ^a   Choi, Jaehwa ^a  

^a GEORGE WASHINGTON UNIVERSITY   (United States)

Author keywords

Expected a posterior; Maximum likelihood; Polychoric correlation

Indexed keywords

EID: 78650784672     PISSN: 15389472     EISSN: 15389472     Source Type: Journal    
DOI: 10.22237/jmasm/1241137860     Document Type: Article

References (16)

1. Babakus, E., & Ferguson, C. E. (1988). On choosing the appropriate measure of association when analyzing rating scale data. Academy of Marketing Science, 16(1), 95-103.
2. Bandalos, D. L. (2006). The use of Monte Carlo studies in structural equation modeling research. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 385-427). Charlotte, NC: Information Age Publishing Inc.
3. Bock, R. D., & Aitken, M. (1981). Marginal maximum likelihood estimation of item parameters: An application of an EM algorithm. Psychometrika, 46, 443-459.
4. Bollen, K. A., & Barb, K. H. (1981). Pearson's r and coarsely categorized measures. American Sociological Review, 46, 232-239.
5. Casella, G., & Berger, R. L. (2002). Statistical inference (2nd Ed.). Pacific Grove, CA: Duxbury.
6. Chen, S.-K., Hou, L., & Dodd, B. G. (1998). A comparison of maximum likelihood estimation and expected a posteriori estimation in CAT using the partial credit model. Educational and Psychological Measurement, 58(4), 569-595.
7. Choi, J., Chen, J., & Kim, S. (in press). EAPPCC: A Matlab subroutine for estimating polychoric correlation matrices using an expected a posteriori estimation method. Applied Psychological Measurement.
8. Flora, D. B. (2002). Evaluation of categorical variable methodology for confirmatory factor analysis with Likert-type data. Unpublished Doctoral dissertation, University of North Carolina at Chapel Hill, Chapel Hill.
9. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466-491.
10. Jöreskog, K. G. (2002-2005). Structural equation modeling with ordinal variables using LISREL. Retrieved 10/07/2007, from http://www.ssicentral.com/lisrel/techdocs/ordinal.pdf.
11. Mislevy, R. J., & Stocking, M. L. (1989). A consumer's guide to LOGIST and BILOG. Applied Psychological Measurement, 13(1), 57-75.
12. Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44, 443-460.
13. Quiroga, A. M. (1992). Studies of the polychoric correlation and other correlation measures for ordinal variables. Unpublished Doctoral dissertation, Acta Universitatis Upsaliensis.
14. Rigdon, E. E., & Ferguson, C. E. (1991). The performance of the polychoric correlation coefficient and selected fitting functions in confirmatory factor analysis with ordinal data. Journal of Marketing Research, 28(4), 491-497.
15. SAS Institute Inc. (2004). SAS/STAT 9.1 user's guide. Cary, NC: SAS Institute Inc.
16. The MathWorks Inc. (2007). Documentation for MathWorks Products, R2007b [Electronic Version]. Retrieved 10/07/2007 from http://www.mathworks.com/access/helpdesk/help/helpdesk.html.