Erratum to: Structural equation modeling with small sample sizes using two-stage ridge least-squares estimation (Behav Res, 10.3758/s13428-012-0206-0);Structural equation modeling with small sample sizes using two-stage ridge least-squares estimation

Behavior Research Methods

Volumn 45, Issue 1, 2013, Pages 75-81

  Jung, Sunho ^a  

^a Kyung Hee University   (South Korea)

Author keywords

Misspecification; Ridge type regularization; Small sample size; Structural equation models; Two stage least squares; Two stage ridge least squares

Indexed keywords

EID: 84874390594     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-012-0297-7     Document Type: Erratum

References (30)

1. Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
2. Bollen, K. A. (1995). Structural equation models that are nonlinear in latent variables: A least squares estimator. Sociological Methodology, 25, 223-251.
3. Bollen, K. A. (1996). An alternative two stage least squares (2SLS) estimator for latent variable equations. Psychometrika, 61, 109-121.
4. Bollen, K. A., & Bauer, D. J. (2004). Automating the selection of model-implied instrumental variables. Sociological Methods and Research, 32, 425-452.
5. Bollen, K. A., Kirby, J. B., Curran, P. J., Paxton, P., & Chen, F. (2007). Latent variable models under misspecification. Two-stage least squares (2SLS) and maximum likelihood (ML) estimators. Sociological Methods and Research, 36, 48-86.
6. Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In R. Cudeck, S. Du Toit, & D. Sorbom (Eds.), Structural equation modeling: Present and future (pp. 139-168). Chicago, IL: SSI Scientific Software.
7. Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90, 443-450.
8. Budaev, S. V. (2010). Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology, 116, 472-480.
9. Buse, A. (1992). The bias of instrumental variable estimators. Econometrica, 60, 173-180.
10. Dimitruk, P., Schermelleh-Engel, K., Kelava, A., & Moosbrugger, H. (2007). Challenges in nonlinear structural equation modeling. Methodology, 3, 100-114.
11. Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Philadelphia, PA: SIAM.
12. Friedman, J. (1989). Penalized discriminant analysis. Journal of the American Statistical Association, 84, 165-175.
13. Groß, J. (2003). Linear regression. Berlin, Germany: Springer.
14. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
15. Hayduk, L., Cummings, G. G., Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! testing! one, two three: Testing the theory in structural equation models. Personality and Individual Differences, 42, 841-850.
16. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55-67.
17. Johnston, J. (1984). Econometric methods. New York, NY: McGraw-Hill.
18. Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system. In A. S. Goldberger & O. D. Duncan (Eds.), Structural equation models in the social sciences (pp. 85-112). New York, NY: Academic Press.
19. Jung, S., & Lee, S. (2011). Exploratory factor analysis for small samples. Behavior Research Methods, 43, 701-709.
20. Kaplan, D. (2009). Structural equation modeling: Foundations and extensions (2nd ed.). Newbury Park, CA: Sage.
21. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford.
22. Le Cessi, S., & van Houwelingen, J. C. (1992). Ridge estimators in logistic regression. Applied Statistics, 41, 191-201.
23. McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model. British Journal of Mathematical and Statistical Psychology, 37, 234-251.
24. Molinaro, A. M., Simon, R., & Pfeiffer, R. M. (2005). Prediction error estimation: A comparison of resampling methods. Bioinformatics, 21, 3301-3307.
25. Mood, A. M., Graybill, F. A., & Boes, D. C. (1974). Introduction to the theory of statistics. New York, NY: McGraw-Hill.
26. Oczkowski, E. (2002). Discriminating between measurement scales using non-nested tests and 2SLS: Monte Carlo evidence. Structural Equation Modeling, 9, 103-125.
27. Pagès, J., & Tenenhaus, M. (2001). Multiple factor analysis and PLS path modeling. Chemometrics and Intelligent Laboratory Systems, 58, 261-273.
28. Tenenhaus, M. (2008). Structural equation modeling for small samples (Working Paper No. 885). Paris, France: Jouy-en-Josas.
29. Takane, Y., & Jung, S. (2008). Regularized partial and/or constrained redundancy analysis. Psychometrika, 73, 671-690.
30. Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76, 257-284.