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Hilgeman, Michelle M.; Durkin, Daniel W.; Sun, Fei; DeCoster, Jamie; Allen, Rebecca S.; Gallagher-Thompson, Dolores; Burgio, Louis D. – Gerontologist, 2009
Purpose: The primary aim of this study was to test the stress process model (SPM; Pearlin, Mullan, Semple, & Skaff, 1990) in a racially diverse sample of Alzheimer's caregivers (CGs) using structural equation modeling (SEM) and regression techniques. A secondary aim was to examine race or ethnicity as a moderator of the relation between latent…
Descriptors: Ethnicity, Structural Equation Models, Academic Achievement, Caregivers
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Archambault, Isabelle; Janosz, Michel; Fallu, Jean-Sebastien; Pagani, Linda S. – Journal of Adolescence, 2009
Although the concept of school engagement figures prominently in most school dropout theories, there has been little empirical research conducted on its nature and course and, more importantly, the association with dropout. Information on the natural development of school engagement would greatly benefit those interested in preventing student…
Descriptors: Structural Equation Models, Dropouts, At Risk Persons, Factor Analysis
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Rousseau, Denise M.; Hornung, Severin; Kim, Tai Gyu – Journal of Vocational Behavior, 2009
This study tests propositions regarding idiosyncratic deals (i-deals) in a sample of N = 265 hospital employees using structural equation modeling. Timing and content of idiosyncratic employment arrangements are postulated to have differential consequences for the nature of the employment relationship. Results confirm that i-deals made after hire…
Descriptors: Employment Patterns, Structural Equation Models, Correlation, Industrial Psychology
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Preacher, Kristopher J. – Multivariate Behavioral Research, 2006
Fitting propensity (FP) is defined as a model's average ability to fit diverse data patterns, all else being equal. The relevance of FP to model selection is examined in the context of structural equation modeling (SEM). In SEM it is well known that the number of free model parameters influences FP, but other facets of FP are routinely excluded…
Descriptors: Structural Equation Models, Case Studies, Selection
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Raykov, Tenko; Marcoulides, George A. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
A covariance structure modeling perspective on reliability estimation can be used to construct a formal approach to estimation of reliability in multilevel models. This article presents a didactic discussion of the relation between a structural modeling procedure for scale reliability estimation and the notion of reliability of observed means in…
Descriptors: Structural Equation Models, Reliability, Interdisciplinary Approach
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Stoel, Reinoud D.; Garre, Francisca Galindo; Dolan, Conor; van den Wittenboer, Godfried – Psychological Methods, 2006
The authors show how the use of inequality constraints on parameters in structural equation models may affect the distribution of the likelihood ratio test. Inequality constraints are implicitly used in the testing of commonly applied structural equation models, such as the common factor model, the autoregressive model, and the latent growth…
Descriptors: Testing, Structural Equation Models, Evaluation Methods
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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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Chien, Nina C.; Mistry, Rashmita S. – Child Development, 2013
The effects of geographic variations in cost of living and family income on children's academic achievement and social competence in first grade (mean age = 86.9 months) were examined, mediated through material hardship, parental investments, family stress, and school resources. Using data from the Early Childhood Longitudinal Study-Kindergarten…
Descriptors: Geographic Location, Family Income, Economic Climate, Interpersonal Competence
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Marsh, Herbert W.; Nagengast, Benjamin; Morin, Alexandre J. S.; Parada, Roberto H.; Craven, Rhonda G.; Hamilton, Linda R. – Journal of Educational Psychology, 2011
Existing research posits multiple dimensions of bullying and victimization but has not identified well-differentiated facets of these constructs that meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors that are not so highly correlated as to detract…
Descriptors: Locus of Control, Test Bias, Bullying, Structural Equation Models
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