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Direct linkSavalei, Victoria – Psychological Methods, 2010
Maximum likelihood is the most common estimation method in structural equation modeling. Standard errors for maximum likelihood estimates are obtained from the associated information matrix, which can be estimated from the sample using either expected or observed information. It is known that, with complete data, estimates based on observed or…
Descriptors: Structural Equation Models, Computation, Error of Measurement, Data
Savalei, Victoria; Yuan, Ke-Hai – Multivariate Behavioral Research, 2009
Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) for general covariance structure models and applied to structural equation modeling by Bollen and Stine…
Descriptors: Statistical Inference, Goodness of Fit, Structural Equation Models, Transformations (Mathematics)
Download full textCarter, Rufus Lynn – Research & Practice in Assessment, 2006
Many times in both educational and social science research it is impossible to collect data that is complete. When administering a survey, for example, people may answer some questions and not others. This missing data causes a problem for researchers using structural equation modeling (SEM) techniques for data analyses. Because SEM and…
Descriptors: Structural Equation Models, Error of Measurement, Data, Change Strategies
