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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
Bickel, Robert – Guilford Publications, 2007
This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical…
Descriptors: Regression (Statistics), Social Sciences, Statistical Analysis, Structural Equation Models
McGinley, Meredith; Carlo, Gustavo – Journal of Youth and Adolescence, 2007
The direct and indirect relations between six types of prosocial behavior and physical aggression were examined. Data were gathered from 252 college students (M age = 21.67 years; 184 women) who completed measures of sympathy, prosocial behavior, and physical aggression. Structural equation modeling revealed that sympathy fully mediated the…
Descriptors: Structural Equation Models, Prosocial Behavior, Altruism, Aggression
McCoach, D. Betsy; Black, Anne C.; O'Connell, Ann A. – Psychology in the Schools, 2007
Although structural equation modeling (SEM) is one of the most comprehensive and flexible approaches to data analysis currently available, it is nonetheless prone to researcher misuse and misconceptions. This article offers a brief overview of the unique capabilities of SEM and discusses common sources of user error in drawing conclusions from…
Descriptors: Misconceptions, Inferences, Data Analysis, Structural Equation Models
Diefendorff, James M.; Mehta, Kajal – Journal of Applied Psychology, 2007
The authors developed and tested new theoretical relations between approach and avoidance motivational traits and deviant work behaviors. Approach motivation was divided into 3 traits: personal mastery (i.e., desire to achieve), competitive excellence (i.e., desire to perform better than others), and behavioral activation system (BAS) sensitivity…
Descriptors: Stimuli, Structural Equation Models, Student Motivation, Rewards
Schmiedek, Florian; Oberauer, Klaus; Wilhelm, Oliver; Suss, Heinz-Martin; Wittmann, Werner W. – Journal of Experimental Psychology: General, 2007
The authors bring together approaches from cognitive and individual differences psychology to model characteristics of reaction time distributions beyond measures of central tendency. Ex-Gaussian distributions and a diffusion model approach are used to describe individuals' reaction time data. The authors identified common latent factors for each…
Descriptors: Psychometrics, Memory, Structural Equation Models, Reaction Time
Elosua, Paula – Journal of Vocational Behavior, 2007
This article proposes the Thurstonian paired comparison model to assess vocational preferences and uses this approach to evaluate the Realistic, Investigative, Artistic, Social, Enterprise, and Conventional (RIASEC) model in the Basque Country (Spain). First, one unrestricted model is estimated in the Structural Equation Modelling framework using…
Descriptors: Foreign Countries, Correlation, Structural Equation Models, Vocational Interests
Ferguson, Kristin M.; Mindel, Charles H. – Crime & Delinquency, 2007
This study tested a model of the effects of different predictors on individuals' levels of fear of crime in Dallas neighborhoods. Given its dual focus on individual perceptions and community-level interactions, social capital theory was selected as the most appropriate framework to explore fear of crime within the neighborhood milieu. A structural…
Descriptors: Neighborhoods, Social Support Groups, Crime, Safety
Bollen, Kenneth A.; Maydeu-Olivares, Albert – Psychometrika, 2007
This paper presents a new polychoric instrumental variable (PIV) estimator to use in structural equation models (SEMs) with categorical observed variables. The PIV estimator is a generalization of Bollen's (Psychometrika 61:109-121, 1996) 2SLS/IV estimator for continuous variables to categorical endogenous variables. We derive the PIV estimator…
Descriptors: Structural Equation Models, Simulation, Robustness (Statistics), Computation
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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