Two rules of identification for structural equation models

Structural Equation Modeling

Volumn 16, Issue 3, 2009, Pages 523-536

  Bollen, Kenneth A ^a   Davis, Walter R ^b  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b Statistics ^*  (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL TEST; ESTIMATES OF PARAMETERS; IDENTIFICATION STRATEGIES; PIECE-WISE; STRUCTURAL EQUATION MODELING; STRUCTURAL EQUATION MODELS;

STATISTICAL METHODS;

SOFTWARE TESTING;

EID: 70449595821     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705510903008261     Document Type: Article

References (20)

1. Bajada, C., & Schneider, F. (2005). The shadow economies of the Asia-Pacific. Pacific Economic Review, 10, 379-401.
2. Bekker, P. A., Merckens, A., & Wansbeek, T. J. (1994). Identification, equivalent models, and computer algebra. San Diego, CA: Academic.
3. Bentler, P. M., & Chou, C. P. (1988). Practical issues in structural equation modeling. In J. S. Long (Ed.), Common problems/proper solutions: Avoiding error in survey research (pp. 161-192). Newbury Park, CA: Sage.
4. Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. Sociological Methodology, 17, 37-69.
5. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
6. Bollen, K. A., & Davis, W. R. (2009). Causal indicator models: Identification, estimation, and testing. Structural Equation Modeling, 16(3), 498-522.
7. Brito, C., & Pearl, J. (2002). A new identification condition for recursive models with correlated errors. Structural Equation Modeling, 9, 459-474.
8. Davis, W. R. (1993). The FC1 rule of identification for confirmatory factor analysis: A general sufficient condition. Sociological Methods & Research, 21, 403-437.
9. Fisher, F. M. (1966). The identification problem in econometrics. New York: McGraw-Hill.
10. Hauser, R. M., & Goldberger, A. S. (1971). The treatment of unobservable variables in path analysis. Sociological Methodology, 3, 81-117.
11. Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70, 631-639.
12. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8 user's reference guide. Chicago: Scientific Software International.
13. Kenny, D. A. (1979). Correlation and causality. New York: Wiley.
14. O'Brien, R. M. (1994). Identification of simple measurement models with multiple latent variables and correlated errors. Sociological Methodology, 24, 137-170.
15. Reilly, T., & O'Brien, R. M. (1996). Identification of confirmatory factor analysis models of arbitrary complexity: The side-by-side rule. Sociological Methods & Research, 24, 473-491.
16. Rigdon, E. E. (1995). A necessary and sufficient identification rule for structural models estimated in practice. Multivariate Behavioral Research, 30, 359-383.
17. Robinson, P. M. (1974). Identification, estimation and large-sample theory for regressions containing unobservable variables. International Economic Review, 15, 680-692.
18. Sanchez-Perez, M., & Iniesta-Bonillo, M. A. (2004). Consumers felt commitment towards retailers: Index development and validation. Journal of Business & Psychology, 19, 141-159.
19. Stapleton, D. C. (1978). Analyzing political participation data with a mimic model. Sociological Methodology, 9, 52-74.
20. Wald, A. (1950). A note on the identification of econometric relations. In T. C. Koopmans (Ed.), Statistical inference in dynamic economic models (pp. 238-244). New York: Wiley.