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Kim, Young-Suk Grace – Grantee Submission, 2016
We investigated component language and cognitive skills of oral language comprehension of narrative texts (i.e., listening comprehension). Using the construction--integration model of text comprehension as an overarching theoretical framework, we examined direct and mediated relations of foundational cognitive skills (working memory and…
Descriptors: Foreign Countries, Language Skills, Cognitive Ability, Oral Language
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Anderson, Daniel; Farley, Dan; Tindal, Gerald – Journal of Special Education, 2015
Students with significant cognitive disabilities present an assessment dilemma that centers on access and validity in large-scale testing programs. Typically, access is improved by eliminating construct-irrelevant barriers, while validity is improved, in part, through test standardization. In this article, one state's alternate assessment data…
Descriptors: Mental Retardation, Evaluation Methods, Student Evaluation, Standardized Tests
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Reed, Deborah K.; Petscher, Yaacov; Truckenmiller, Adrea J. – Reading Research Quarterly, 2017
This study explored the relationship between the reading ability and science achievement of students in grades 5, 8, and 9. Reading ability was assessed with four measures: word recognition, vocabulary, syntactic knowledge, and comprehension (23% of all passages were on science topics). Science achievement was assessed with state…
Descriptors: Reading Ability, Science Achievement, Middle School Students, Elementary School Students
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Loken, Eric – Structural Equation Modeling: A Multidisciplinary Journal, 2005
The choice of constraints used to identify a simple factor model can affect the shape of the likelihood. Specifically, under some nonzero constraints, standard errors may be inestimable even at the maximum likelihood estimate (MLE). For a broader class of nonzero constraints, symmetric normal approximations to the modal region may not be…
Descriptors: Inferences, Computation, Structural Equation Models, Factor Analysis
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Enders, Craig K.; Peugh, James L. – Structural Equation Modeling, 2004
Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…
Descriptors: Inferences, Structural Equation Models, Factor Analysis, Error of Measurement