참고문헌
- Ansari, A., Kamel J. and Sharan J. (2000). A hierarchical Bayesian methodology for treating heterogeneity in structural equation models. Marketing Science, 19, 328-347. https://doi.org/10.1287/mksc.19.4.328.11789
- Bagozzi, R. P. and Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40, 8-34. https://doi.org/10.1007/s11747-011-0278-x
- Baron, R. M. and Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173
- Bollen, K. A. (1989). Structural equations with latent variables, John Wiley & Sons, New York.
- Bollen, K. A. (1996). An alternative two stage least squares (2SLS) estimator for latent variable equations. Psychometrika, 61, 109-121. https://doi.org/10.1007/BF02296961
- Bollen, K. A. and Bauer, D. J. (2004). Automating the selection of model-implied instrumental variables. Sociological Methods and Research, 32, 425-452. https://doi.org/10.1177/0049124103260341
- Bollen, K. A., Kirby, J. B., Curran, P. J., Paxton, P. and 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. https://doi.org/10.1177/0049124107301947
- Boomsma, A. and Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In Structural Equation Modeling: Present and Future (Cudeck, R., Du Toit, S. & Sorbom, D., eds), SSI Scientific Software, Chicago, 139-168.
- Budaev, S. V. (2010). Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology, 116, 472-480. https://doi.org/10.1111/j.1439-0310.2010.01758.x
- Chen, F., Bollen, K., Paxton, P., Curran, P. J. and Kirby, J. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological Methods and Research, 29, 468-508. https://doi.org/10.1177/0049124101029004003
- Choi, H. S., Kwon, Y. J. and Ha, J. C. (2013). Study of university students' perceptions on participation in elections via structural equation model-Focusing on K university students. Journal of the Korean Data & Information Science Society, 24, 379-390. https://doi.org/10.7465/jkdi.2013.24.2.379
- Cohen, J. (1988). Statistical power for the behavioral sciences, 2nd ed., Lawrence Erlbaum, New Jersey.
- Efron, B. (1982). The jackknife, the bootstrap and other resampling Plans, SIAM, Philadelphia.
- Fan, X. and Sivo, S. A. (2005). Sensitivity of fit indices to misspecified structural or measurement model components: Rationale of two-index strategy revisited. Structural Equation Modeling, 12, 343-367. https://doi.org/10.1207/s15328007sem1203_1
- Fox, J. (2006). Structural equation modeling with the sem package in R . Structural Equation Modeling, 13, 465-486. https://doi.org/10.1207/s15328007sem1303_7
- Grace, J. B., Anderson, T. M., Olff, H. and Scheiner, S. M. (2010). On the specification of structural equation models for ecological systems. Ecological Monographs, 80, 67-87. https://doi.org/10.1890/09-0464.1
- Grewal, R., Cote, J. A. and Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23, 519-529. https://doi.org/10.1287/mksc.1040.0070
- Hair, J. F., Ringle, C. M. and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139-151. https://doi.org/10.2753/MTP1069-6679190202
- Hastie, T., Tibshirani, R. and Friedman, J. (2001). The elements of statistical learning; Data mining, inference, and prediction, Sringer-Verlag, New York.
- Hayduk, L., Cummings, G. G., Boadu, K., Pazderka-Robinson, H. and Boulianne, S. (2007). Testing! testing! one, two three-Testing the theory in structural equation models. Personality and Individual Differences, 42, 841-850. https://doi.org/10.1016/j.paid.2006.10.001
- Henseler, J. (2012). Why generalized structured component analysis is not universally preferable to structural equation modeling. Journal of the Academy of Marketing Science, 40, 402-413. https://doi.org/10.1007/s11747-011-0298-6
- Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55-67. https://doi.org/10.1080/00401706.1970.10488634
- Hong, Y., Jang, G. and Choi, C. (2016). Life satisfaction and self-esteem of children from low-income class: Testing mediation model of depression. Journal of the Korean Data & Information Science Society, 27, 179-189. https://doi.org/10.7465/jkdi.2016.27.1.179
- James, L. R., Mulaik, S. A. and Brett, J. M. (2006). A tale of two methods. Organizational Research Methods, 9, 233-244. https://doi.org/10.1177/1094428105285144
- Jarvis, C. B., MacKenzie, S. B. and Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199-218. https://doi.org/10.1086/376806
- Jung, S. (2013). Structural equation modeling with small sample sizes using two-stage least-squares estimation. Behavior Research Methods, 45, 75-81. https://doi.org/10.3758/s13428-012-0206-0
- Kaplan, D. (2009). Structural equation modeling: Foundations and extensions, 2nd ed., Sage, California.
- Lee, S. (2007). Problems caused by model equivalence in developing and testing models. Journal of Educational Evaluation, 20, 125-146.
- McArdle, J. J. and McDonald, R. P. (1984). Some algebraic properties of the reticular action model. British Journal of Mathematical and Statistical Psychology, 37, 234-251. https://doi.org/10.1111/j.2044-8317.1984.tb00802.x
- MacKenzie, S., Podsakoff, P. and Jarvis, C. (2005). The problem of measurement model misspecification in behavioural and organizational research and some recommended solutions. Journal of Applied Psychology, 90, 710-730. https://doi.org/10.1037/0021-9010.90.4.710
- Min, D. K. and Choi, M. K. (2016). How depression affects girls who experienced violence in home or at school: Using mixed model. Journal of the Korean Data & Information Science Society, 27, 101-110. https://doi.org/10.7465/jkdi.2016.27.1.101
- Olsson, U. H., Foss, T., Troye, S. and Howell, R. (2000). The Performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and non-normality. Structural Equation Modeling, 7, 557-595. https://doi.org/10.1207/S15328007SEM0704_3
- Sideridis, G., Simos, P., Papanicolaou, A. and Fletcher, J. (2014). Using structural equation modeling to assess functional connectivity in the brain: Power and sample size considerations. Educational & Psychological Measurement, 74, 733-758. https://doi.org/10.1177/0013164414525397
- Steenkamp, J. B. E. M. and Baumgartner, H. (2000). On the use of structural equation models for marketing modeling. International Journal of Research in Marketing, 17, 195-202. https://doi.org/10.1016/S0167-8116(00)00016-1
- Tenenhaus, M., Pages, J., Ambroisine, L. and Guinot, C. (2005). PLS methodology to study relationships between hedonic judgments and product characteristics. Food Quality and Preference, 16, 315-325. https://doi.org/10.1016/j.foodqual.2004.05.013
- Tenenhaus, M. (2008). Structural equation modeling for small samples, HEC school of management (GRECHEC).
피인용 문헌
- A Comparison of Regularized Maximum-Likelihood, Regularized 2-Stage Least Squares, and Maximum-Likelihood Estimation with Misspecified Models, Small Samples, and Weak Factor Structure vol.56, pp.4, 2021, https://doi.org/10.1080/00273171.2020.1753005