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Geiser, Christian; Eid, Michael; Nussbeck, Fridtjof W. – Psychological Methods, 2008
In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006, see EJ751121) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an…
Descriptors: Factor Structure, Factor Analysis, Structural Equation Models, Questionnaires
Ferrier-Lynn, Melissa; Skouteris, Helen – Australian Journal of Early Childhood, 2008
This study examined parent cognitions and parent-infant interaction in terms of their contribution to infant development in the first 12 months. With a sample of 95 mother-infant dyads, results using structural equation modelling confirmed the expected finding that parent-infant interaction mediates the association between parent cognitions and…
Descriptors: Infants, Parent Child Relationship, Schemata (Cognition), Mothers
Eid, Michael; Nussbeck, Fridtjof W.; Geiser, Christian; Cole, David A.; Gollwitzer, Mario; Lischetzke, Tanja – Psychological Methods, 2008
The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods…
Descriptors: Structural Equation Models, Multitrait Multimethod Techniques, Statistical Analysis, Error of Measurement
Davis-Kean, Pamela E.; Huesmann, L. Rowell; Jager, Justin; Collins, W. Andrew; Bates, John E.; Lansford, Jennifer E. – Child Development, 2008
Many social science theories that examine the connection between beliefs and behaviors assume that belief constructs will predict behaviors similarly across development. Converging research implies that this assumption may not be tenable across all ages or all belief constructs. Thus, to test this implication, the relation between behavior and…
Descriptors: Structural Equation Models, Self Efficacy, Beliefs, Child Development
Savalei, Victoria – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Normal theory maximum likelihood (ML) is by far the most popular estimation and testing method used in structural equation modeling (SEM), and it is the default in most SEM programs. Even though this approach assumes multivariate normality of the data, its use can be justified on the grounds that it is fairly robust to the violations of the…
Descriptors: Structural Equation Models, Testing, Factor Analysis, Maximum Likelihood Statistics
Berninger, Virginia W.; Nielsen, Kathleen H.; Abbott, Robert D.; Wijsman, Ellen; Raskind, Wendy – Journal of School Psychology, 2008
The International Dyslexia Association defines dyslexia as unexpected problems of neurobiological origin in accuracy and rate of oral reading of single real words, single pseudowords, or text or of written spelling. However, prior research has focused more on the reading than the spelling problems of students with dyslexia. A test battery was…
Descriptors: Letters (Correspondence), Writing (Composition), Spelling, Oral Reading
Fontaine, Reid Griffith; Yang, Chongming; Dodge, Kenneth A.; Bates, John E.; Pettit, Gregory S. – Child Development, 2008
This study examined the bidirectional development of aggressive response evaluation and decision (RED) and antisocial behavior across five time points in adolescence. Participants (n = 522) were asked to imagine themselves behaving aggressively while viewing videotaped ambiguous provocations and answered a set of RED questions following each…
Descriptors: Aggression, Structural Equation Models, Antisocial Behavior, Adolescents
Velazquez, Cesareo Morales – Computers in the Schools, 2008
Data from Mexico City, Mexico (N = 978) and from Texas, USA (N = 932) were used to test the predictive validity of the teacher professional development component of the Will, Skill, Tool Model of Technology Integration in a cross-cultural context. Structural equation modeling (SEM) was used to test the model. Analyses of these data yielded…
Descriptors: Structural Equation Models, Technology Integration, Predictive Validity, Foreign Countries
Kohen, Dafna E.; Leventhal, Tama; Dahinten, V. Susan; McIntosh, Cameron N. – Child Development, 2008
The present study used Canadian National Longitudinal data to examine a model of the mechanisms through which the effects of neighborhood socioeconomic conditions impact young children's verbal and behavioral outcomes (N = 3,528; M age = 5.05 years, SD= 0.86). Integrating elements of social disorganization theory and family stress models, and…
Descriptors: Neighborhoods, Structural Equation Models, Disadvantaged, Young Children
Peer reviewedNewsom, 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
Peer reviewedHancock, 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
Peer reviewedMarcoulides, 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
Peer reviewedRaykov, 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
Peer reviewedRubio, 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
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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