Alternative approaches to structural modeling of ordinal data: A monte carlo study

Structural Equation Modeling

Volumn 4, Issue 4, 1997, Pages 261-282

  Coenders, Germà ^a   Satorra, Albert S ^b   Saris, Willem E ^c  

^a UNIVERSITY OF GIRONA   (Spain)

^b UNIVERSITAT POMPEU FABRA   (Spain)

^c UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000330243     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705519709540077     Document Type: Article

References (33)

1. Babakus, E., Ferguson, C. R., & Jöreskog, K. G. (1987). The sensitivity of confirmatory maximum likelihood factor analysis to violations of measurement scale and distributional assumptions. Journal of Marketing Research 24, 222-229.
2. Bagozzi, R. P. (1994). Structural equation models in marketing research: basic principles. In R. P. Bagozzi (Ed.), Principles of marketing research (pp. 317-385). Cambridge, MA: Basil Blackwell.
3. Bentler, P. M. (1989). EQS structural equation program manual. Los Angeles: BMDP Statistical Software.
4. Bermann, G. (1993). Estimation and inference in bivariate and multhariate ordinal PROBIT models. Doctoral dissertation, University of Uppsala, Sweden.
5. Bernstein, I. H., & Teng, G. (1989). Factoring items and factoring scales are different: spurious evidence for multidimensionality due to item categorization. Psychological Bulletin, 105, 467-477.
6. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
7. Bollen, K. A., & Barb, K. H. (1981). Pearson's r and coarsely categorized data. American Sociological Review, 46, 232-239.
8. Coenders, G., & Saris, W. E. (1995). Categorization and measurement quality. The choice between Pearson and polychoric correlations. In W. E. Saris & Á. Münnich (Eds.), The multitrait-multimethod approach to evaluate measurement instruments (pp. 125-144). Budapest, Hungary: Eötvös University Press.
9. 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.
10. Homer, P., & O'Brien, R. M. (1988). Using LISREL models with crude rank category measures. Quality and Quantity, 22, 191-201.
11. Johnson, D. R., & Creech, J. C. (1983). Ordinal measures in multiple indicator models: a simulation study of categorization error. American Sociological Review, 48, 398-407.
12. Jöreskog, K. G. (1990). New developments in LISREL. Analysis of ordinal variables using polychoric correlations and weighted least squares. Quality and Quantity, 24, 387-404.
13. Jöreskog, K. G., & Sörbom D. (1988). PREUS: A program for multivariate data screening and data summarization. A preprocessor for LISREL. Mooresville, IN: Scientific Software.
14. Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7: A guide to the program and applications. Chicago: SPSS.
15. Jöreskog, K. G., & Sörbom, D. (1993a). New features in LISREL 8. Chicago: Scientific Software International.
16. Jöreskog, K. G., & Sörbom, D. (1993b). New features in PREUS 2. Chicago: Scientific Software International.
17. Maddala, G. S. (1983). Limited dependent and qualitative variables in econometrics. Cambridge, England: Cambridge University Press.
18. Minitab Inc. (1991). MINITAB reference manual (Release 8., PC version). State College, PA: Author.
19. Muthén, B. (1979). A structural PROBIT model with latent variables. Journal of the American Statistical Association, 74, 807-811.
20. Muthén, B. (1983). Latent variable structural equation modeling with categorical data. Journal of Econometrics, 22, 43-65.
21. Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical and continuous latent variable indicators. Psychometrika, 43, 241-250.
22. Muthén, B. (1987). LISCOMP. Analysis of linear structural equations with a comprehensive measurement model. Theoretical integration and user's guide. Mooresville, In: Scientific Software.
23. Muthén, B., & Satorra, A. (1995). Technical aspects of Muthén's LISCOMP approach to estimation of latent variable relations with a comprehensive measurement model. Psychometrika, 60, 489-503.
24. O'Brien, R. M. (1985). The relationship between ordinal measures and their underlying values: Why all the disagreement? Quality and Quantity, 19, 265-277.
25. O'Brien, R.M., & Homer, P. (1987). Correction for coarsely categorized measures. LISREL'spolyserial and polychoric correlations. Quality and Quantity, 21, 349-360.
26. Olsson, U. (1979a). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44, 443-460.
27. Olsson, U. (1979b). On the robustness of factor analysis against crude categorization of observations. Multivariate Behavioral Research 14, 485-500.
28. Olsson, U., Drasgow, F., & Dorans, N. J. (1982). The polyserial correlation coefficient. Psychometrika, 47, 337-347.
29. Parry, C. D. H., & McArdle, J. J. (1991). An applied comparison of methods for least-squares factor analysis of dichotomous variables. Applied Psychological Measurement, 15, 35-46.
30. Quiroga, A. M. (1992). Studies of the polychoric correlation and other correlation measures for ordinal variables. Doctoral dissertation, University of Uppsala, Sweden.
31. SAS Institute. (1990). CALIS and LOGISTIC procedures (SAS Tech. Rep. P-200). Cary, NC: SAS/STAT Software.
32. Satorra, A. (1990). Robustness issues in structural equation modeling: a review of recent developments. Quality and Quantity, 24, 367-86.
33. Schepers, A., Arminger, G., & Kusters, U. (1992). The analysis of non-metric endogenous variables in latent variable models: The MECOSA approach. In P. Gruber (Ed.), Econometric decision models: New methods of modeling and applications. Lecture notes in mathematical systems (pp. 459-472). Heidelberg, Germany: Springer-Verlag.