Constrained versus unconstrained estimation in structural equation modeling Export

@article{savalei:kolenikov:2008, abstract = {Recently, R. D. Stoel, F. G. Garre, C. Dolan, and G. van den Wittenboer (2006) reviewed approaches for obtaining reference mixture distributions for difference tests when a parameter is on the boundary. The authors of the present study argue that this methodology is incomplete without a discussion of when the mixtures are needed and show that they only become relevant when constrained difference tests are conducted. Because constrained difference tests can hide important model misspecification, a reliable way to assess global model fit under constrained estimation would be needed. Examination of the options for assessing model fit under constrained estimation reveals that no perfect solutions exist, although the conditional approach of releasing a degree of freedom for each active constraint appears to be the most methodologically sound one. The authors discuss pros and cons of constrained and unconstrained estimation and their implementation in 5 popular structural equation modeling packages and argue that unconstrained estimation is a simpler method that is also more informative about sources of misfit. In practice, researchers will have trouble conducting constrained difference tests appropriately, as this requires a commitment to ignore Heywood cases. Consequently, mixture distributions for difference tests are rarely appropriate.}, author = {Savalei, Victoria and Kolenikov, Stanislav}, citeulike-article-id = {4307766}, citeulike-linkout-0 = {http://psycnet.apa.org/?fa=main.doiLanding&doi=10.1037/1082-989X.13.2.150}, citeulike-linkout-1 = {http://dx.doi.org/10.1037/1082-989X.13.2.150}, doi = {10.1037/1082-989X.13.2.150}, journal = {Psychological Methods}, keywords = {boundary, heywood\_case, latent\_variables, sem}, number = {2}, pages = {150--170}, posted-at = {2009-04-14 15:53:08}, priority = {2}, title = {Constrained versus unconstrained estimation in structural equation modeling}, url = {http://dx.doi.org/10.1037/1082-989X.13.2.150}, volume = {13}, year = {2008} }

TY - JOUR ID - savalei:kolenikov:2008 L3 - citeulike-article-id:4307766 N2 - Recently, R. D. Stoel, F. G. Garre, C. Dolan, and G. van den Wittenboer (2006) reviewed approaches for obtaining reference mixture distributions for difference tests when a parameter is on the boundary. The authors of the present study argue that this methodology is incomplete without a discussion of when the mixtures are needed and show that they only become relevant when constrained difference tests are conducted. Because constrained difference tests can hide important model misspecification, a reliable way to assess global model fit under constrained estimation would be needed. Examination of the options for assessing model fit under constrained estimation reveals that no perfect solutions exist, although the conditional approach of releasing a degree of freedom for each active constraint appears to be the most methodologically sound one. The authors discuss pros and cons of constrained and unconstrained estimation and their implementation in 5 popular structural equation modeling packages and argue that unconstrained estimation is a simpler method that is also more informative about sources of misfit. In practice, researchers will have trouble conducting constrained difference tests appropriately, as this requires a commitment to ignore Heywood cases. Consequently, mixture distributions for difference tests are rarely appropriate. IS - 2 JF - Psychological Methods EP - 170 TI - Constrained versus unconstrained estimation in structural equation modeling VL - 13 SP - 150 KW - boundary KW - heywood_case KW - latent_variables KW - sem AU - Savalei, Victoria AU - Kolenikov, Stanislav PY - 2008/// UR - http://dx.doi.org/10.1037/1082-989X.13.2.150 ER -