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Powell, Daisy; Stainthorp, Rhona; Stuart, Morag; Garwood, Holly; Quinlan, Philip – Journal of Experimental Child Psychology, 2007
Two studies investigated the degree to which the relationship between rapid automatized naming (RAN) performance and reading development is driven by shared phonological processes. Study 1 assessed RAN, phonological awareness, and reading performance in 1010 7- to 10-year-olds. Results showed that RAN deficits occurred in the absence of…
Descriptors: Program Effectiveness, Phonology, Structural Equation Models, Reading Skills
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Willoughby, Michael; Vandergrift, Nathan; Blair, Clancy; Granger, Douglas A. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
This study introduces a novel application of structural equation modeling (SEM) for the analysis of cortisol data that are collected using a pre-post-post design. By way of an extended example, an SEM model is developed that permits an examination of both the overall level of cortisol, as well as changes in cortisol (reactivity and regulation), as…
Descriptors: Disadvantaged Youth, Structural Equation Models, Cognitive Ability, Preschool Children
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Newsom, Jason T. – Structural Equation Modeling, 2002
Proposes a novel structural modeling approach based on latent growth curve model specifications for use with dyadic data. The approach allows researchers to test more sophisticated causal models, incorporate latent variables, and estimate more complex error structures than is currently possible using hierarchical linear modeling or multilevel…
Descriptors: Structural Equation Models
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Hancock, Gregory R. – Structural Equation Modeling, 1999
Proposes an analog to the Scheffe test (H. Scheffe, 1953) to be applied to the exploratory model-modification scenario. The method is a sequential finite-intersection multiple-comparison procedure that controls the Type I error rate to a desired alpha level across all possible post hoc model modifications. (SLD)
Descriptors: Structural Equation Models
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Marcoulides, George A.; Drezner, Zvi; Schumacker, Randall E. – Structural Equation Modeling, 1998
Introduces an alternative structural equation modeling (SEM) specification search approach based on the Tabu search procedure. Using data with known structure, the procedure is illustrated, and its capabilities for specification searches in SEM are demonstrated. (Author/SLD)
Descriptors: Structural Equation Models
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Raykov, Tenko – Structural Equation Modeling, 2000
Provides counterexamples where the covariance matrix provides crucial information about consequential model misspecifications and cautions researchers about overinterpreting the conclusion of D. Rogosa and J. Willett (1985) that the covariance matrix is a severe summary of longitudinal data that may discard crucial information about growth. (SLD)
Descriptors: Structural Equation Models
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Rubio, Doris McGartland; Berg-Weger, Marla; Tebb, Susan S. – Structural Equation Modeling, 2001
Illustrates how structural equation modeling can be used to test the multidimensionality of a measure. Using data collected on a multidimensional measure, compares an oblique factor model with a higher order factor model, and shows how the oblique factor model fits the data better. (SLD)
Descriptors: Structural Equation Models
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Vautier, Stephane; Steyer, Rolf; Jmel, Said; Raufaste, Eric – Structural Equation Modeling, 2005
How is affective change rated with positive adjectives such as good related to change rated with negative adjectives such as bad? Two nested perfect and imperfect forms of dynamic bipolarity are defined using latent change structural equation models based on tetrads of items. Perfect bipolarity means that latent change scores correlate -1.…
Descriptors: Structural Equation Models
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Fan, Xitao; Sivo, Stephen A. – Structural Equation Modeling, 2005
In previous research (Hu & Bentler, 1998, 1999), 2 conclusions were drawn: standardized root mean squared residual (SRMR) was the most sensitive to misspecified factor covariances, and a group of other fit indexes were most sensitive to misspecified factor loadings. Based on these findings, a 2-index strategy-that is, SRMR coupled with another…
Descriptors: Structural Equation Models
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Frenzel, Anne C.; Goetz, Thomas; Ludtke, Oliver; Pekrun, Reinhard; Sutton, Rosemary E. – Journal of Educational Psychology, 2009
In this study, the authors examined the relationship between teacher and student enjoyment. Based on social-cognitive approaches to emotions, they hypothesized (a) that teacher enjoyment and student enjoyment within classrooms are positively linked and (b) that teacher enthusiasm mediates the relationship between teacher and student enjoyment.…
Descriptors: Middle School Students, Student Attitudes, Structural Equation Models, Grade 7
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Borja, Susan E.; Callahan, Jennifer L. – Journal of Child Sexual Abuse, 2009
This investigation sought to operationalize a comprehensive theoretical model, the Trauma Outcome Process Assessment, and test it empirically with structural equation modeling. The Trauma Outcome Process Assessment reflects a robust body of research and incorporates known ecological factors (e.g., family dynamics, social support) to explain…
Descriptors: Structural Equation Models, Depression (Psychology), Family Relationship, Social Support Groups
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Marsh, Herbert W.; Ludtke, Oliver; Trautwein, Ulrich; Morin, Alexandre J. S. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing…
Descriptors: Structural Equation Models, Goodness of Fit, Profiles, Prediction
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Park, Sung Youl – Educational Technology & Society, 2009
Many universities implement e-learning for various reasons. It is obvious that the number of e-learning opportunities provided by higher educational institutes continues to grow in Korea. Yet little research has been done to verify the process of how university students adopt and use e-learning. A sample of 628 university students took part in the…
Descriptors: Electronic Learning, Structural Equation Models, Self Efficacy, Intention
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Ayer, Lynsay; Althoff, Robert; Ivanova, Masha; Rettew, David; Waxler, Ellen; Sulman, Julie; Hudziak, James – Journal of Child Psychology and Psychiatry, 2009
Background: The Child Behavior Checklist Juvenile Bipolar Disorder (CBCL-JBD) profile and Posttraumatic Stress Problems (CBCL-PTSP) scale have been used to assess juvenile bipolar disorder (JBD) and posttraumatic stress disorder (PTSD), respectively. However, their validity is questionable according to previous research. Both measures are…
Descriptors: Check Lists, Behavior Problems, Structural Equation Models, Posttraumatic Stress Disorder
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Frost, David M.; Meyer, Ilan H. – Journal of Counseling Psychology, 2009
The authors examined the associations between internalized homophobia, outness, community connectedness, depressive symptoms, and relationship quality among a diverse community sample of 396 lesbian, gay, and bisexual (LGB) individuals. Structural equation models showed that internalized homophobia was associated with greater relationship problems…
Descriptors: Structural Equation Models, Homosexuality, Depression (Psychology), Social Bias
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