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Sass, Daniel A.; Castro-Villarreal, Felicia; Wilkerson, Steve; Guerra, Norma; Sullivan, Jeremy – Review of Higher Education, 2018
Student retention models were tested via structural equation modeling to examine the interrelations and predictability among socioeconomic status, psychosocial, and student success variables with a sample of 445 undergraduate students attending a large Hispanic serving institution. The proposed theoretical model included socioeconomic status…
Descriptors: Undergraduate Students, Hispanic American Students, Structural Equation Models, Predictor Variables
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Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…
Descriptors: Structural Equation Models, Mixed Methods Research, Statistical Analysis, Sampling
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Grimm, Kevin J.; An, Yang; McArdle, John J.; Zonderman, Alan B.; Resnick, Susan M. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Latent difference score models (e.g., McArdle & Hamagami, 2001) are extended to include effects from prior changes to subsequent changes. This extension of latent difference scores allows for testing hypotheses where recent changes, as opposed to recent levels, are a primary predictor of subsequent changes. These models are applied to…
Descriptors: Memory, Older Adults, Brain, Structural Equation Models