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Peer reviewedde Jong, Peter F. – Structural Equation Modeling, 1999
Describes how a hierarchical regression analysis may be conducted in structural equation modeling. The main procedure is to perform a Cholesky or triangular decomposition of the intercorrelations among the latest predictors. Provides an example of a hierarchical regression analysis with latent variables. (SLD)
Descriptors: Predictor Variables, Regression (Statistics), Structural Equation Models
Peer reviewedKaplan, David – Multivariate Behavioral Research, 1999
Proposes an extension of the propensity score adjustment method to the analysis of group differences on latent variable models. Uses multiple indicators-multiple causes (MIMIC) structural equation modeling to test hypotheses about treatment group differences. Discusses the role of factorial invariance as it relates to this approach. (SLD)
Descriptors: Groups, Hypothesis Testing, Scores, Structural Equation Models
Peer reviewedMarkus, Keith A. – Journal of Experimental Education, 1998
Using a fictional dialog between Tweedledee and Tweedledum (L. Carroll, 1856), the work of H. W. Marsh and K.-T. Hau (1996) on parsimony is interpreted in several ways. Definitions of "judgment" and "rules" are presented and argued before the author concludes that a main thesis of the Marsh article is that judgment is essential…
Descriptors: Goodness of Fit, Standards, Structural Equation Models
Peer reviewedDormann, Christian – Structural Equation Modeling, 2001
Discusses techniques to account for unmeasured third variables in longitudinal designs, introducing a series of less restrictive synchronous common factor models as an extension of the synchronous common factor model. Recommends the use of such models, which can be tested by structural equation modeling, when possible third variables might have…
Descriptors: Factor Structure, Longitudinal Studies, Structural Equation Models
Peer reviewedSivo, Stephen A. – Structural Equation Modeling, 2001
Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…
Descriptors: Longitudinal Studies, Multivariate Analysis, Structural Equation Models
Peer reviewedRaykov, Tenko; Marcoulides, George A. – Structural Equation Modeling, 2001
Outlines a covariance structure analysis approach to the study of parameter trends. Uses the program RAMONA to illustrate the method by fitting a corresponding confirmatory factor analysis model to correlational data from a study involving several psychometric tests and fluid intelligence tasks. (SLD)
Descriptors: Ability, Measures (Individuals), Psychometrics, Structural Equation Models
Peer reviewedWendorf, Craig A. – Structural Equation Modeling, 2002
Compares two statistical approaches for the analysis of data obtained from married couples. Summarizes a current multilevel (or hierarchical) model that has demonstrated usefulness in marital research and respecifies this model into a more familiar structural equation modeling formulation. (SLD)
Descriptors: Data Analysis, Marriage, Spouses, Structural Equation Models
Peer reviewedHancock, Gregory R.; Nevitt, Jonathan – Structural Equation Modeling, 1999
Explains why, when one is using a bootstrapping approach for generating empirical standard errors for parameters of interest, the researchers must choose to fix an indicator path rather than the latent variable variance for the empirical standard errors to be generated properly. (SLD)
Descriptors: Error of Measurement, Identification, Structural Equation Models
Yuan, Ke-Hai – Multivariate Behavioral Research, 2005
Model evaluation is one of the most important aspects of structural equation modeling (SEM). Many model fit indices have been developed. It is not an exaggeration to say that nearly every publication using the SEM methodology has reported at least one fit index. Most fit indices are defined through test statistics. Studies and interpretation of…
Descriptors: Statistics, Structural Equation Models, Goodness of Fit
Rovine, Michael J.; Molenaar, Peter C. M. – Multivariate Behavioral Research, 2005
In this article we show the one-factor model can be rewritten as a quasi-simplex model. Using this result along with addition theorems from time series analysis, we describe a common general model, the nonstationary autoregressive moving average (NARMA) model, that includes as a special case, any latent variable model with continuous indicators…
Descriptors: Revision (Written Composition), Genetics, Structural Equation Models
Gignac, G.E. – Intelligence, 2005
Using a semi-partial correlation approach, Gignac, Stough, and Loukomitis [Gignac, G. E., Stough, C., & Loukomitis, S. (2004). Openness, intelligence, and self-report intelligence. Intelligence, 32, 133-143] examined the relationship between Openness and 'g' and residualized scores from Vocabulary and Information as estimates of crystallized…
Descriptors: Figurative Language, Intelligence, Structural Equation Models, Models
Aberg-Bengtsson, Lisbeth – Scandinavian Journal of Educational Research, 2005
The aim of the present study was to further investigate the properties of a "quantitative" factor previously identified in the "diagrams, tables and maps" subtest of SweSAT. The analyses were carried out with a structural equation modelling technique on the spring 1991 version of SweSAT with 19-year-old test takers and were…
Descriptors: Aptitude Tests, Academic Aptitude, Structural Equation Models
Phakiti, Aek – Language Assessment Quarterly, 2008
This article reports on an empirical study that tests a fourth-order factor model of strategic competence through the use of structural equation modeling (SEM). The study examines the hierarchical relationship of strategic competence to (a) strategic knowledge of cognitive and metacognitive strategy use in general (i.e., trait) and (b) strategic…
Descriptors: Structural Equation Models, Reading Achievement, Reading Tests, Learning Strategies
Ruchkin, Vladislav; Jones, Stephanie; Vermeiren, Robert; Schwab-Stone, Mary – Psychological Assessment, 2008
This study examined the factor structure of the Strengths and Difficulties Questionnaire (SDQ) in urban inner-city and suburban general population samples of American youth. The SDQ was administered to 4,661 predominantly minority urban youth (mean age = 13.0 years, SD = 2.02) and 937 predominantly Caucasian suburban youth (mean age = 14.0 years,…
Descriptors: Emotional Problems, Structural Equation Models, Factor Structure, Measures (Individuals)
Mo, Yun; Singh, Kusum – RMLE Online: Research in Middle Level Education, 2008
This study focused on parents' relationships and involvement in their children's lives and the effects on the students' school engagement and school performance. The study used the Wave I data from the National Longitudinal Study of Adolescent Health (Add Health). The data on seventh and eighth grade students' school and family experiences were…
Descriptors: Parent Participation, Parent School Relationship, Correlation, Academic Achievement

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