Testing Structural Equation Models or Detection of Misspecifications? Export

@article{citeulike:5943923, abstract = {Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the “size” of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test.}, author = {Saris, Willem E. and Satorra, Albert and van der Veld, William M.}, citeulike-article-id = {5943923}, citeulike-linkout-0 = {http://dx.doi.org/10.1080/10705510903203433}, doi = {10.1080/10705510903203433}, journal = {Structural Equation Modeling: A Multidisciplinary Journal}, keywords = {structural-equation-modeling, test-models}, number = {4}, pages = {561--582}, posted-at = {2009-10-15 14:11:55}, priority = {0}, publisher = {Psychology Press}, title = {Testing Structural Equation Models or Detection of Misspecifications?}, url = {http://dx.doi.org/10.1080/10705510903203433}, volume = {16}, year = {2009} }

TY - JOUR ID - citeulike:5943923 L3 - citeulike-article-id:5943923 N2 - Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the “size” of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test. IS - 4 JF - Structural Equation Modeling: A Multidisciplinary Journal EP - 582 TI - Testing Structural Equation Models or Detection of Misspecifications? PB - Psychology Press VL - 16 SP - 561 KW - structural-equation-modeling KW - test-models AU - Saris, Willem AU - Satorra, Albert AU - van der Veld, William PY - 2009/// UR - http://dx.doi.org/10.1080/10705510903203433 ER -