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Sager, Jeffrey K.; Griffeth, Rodger W.; Hom, Peter W. – Journal of Vocational Behavior, 1998
Structural equation modeling was used to test the discriminant validity of three cognitions--thinking of quitting, intent to search, and intent to leave--in 242 sales workers. Results demonstrating a different relationship among the three cognitions were used to revise a 1977 model. (SK)
Descriptors: Intention, Labor Turnover, Sales Occupations, Structural Equation Models
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Lee, Sik-Yum; Shi, Jian-Qing – Structural Equation Modeling, 2000
Extends the LISREL model to incorporate fixed covariates at both the measurement and the structural equations of the model, establishing a Bayesian procedure with conjugate type prior distributions. Illustrates the efficiency of the algorithm and presents a goodness of fit statistic for assessing the proposed model. (SLD)
Descriptors: Bayesian Statistics, Goodness of Fit, Structural Equation Models
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Boomsma, Anne – Structural Equation Modeling, 2000
Provides advice on writing a research paper when structural equation models are being used in empirical work. Focuses on what information should be reported and what can be deleted without much loss of judgment about the quality of the research and validity of conclusions. (SLD)
Descriptors: Research Reports, Structural Equation Models, Technical Writing, Validity
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Ghisletta, Paolo; McArdle, John J. – Structural Equation Modeling, 2001
Describes some applications of latent growth curve models in the context of structural equation modeling using data from P. Trickett and F. Putnam (1993) on the physical height of abused (n=77) and nonabused (n=75) adolescent girls. Presents power calculations for the ability of the different models to discern the growth of the abuse sample from…
Descriptors: Adolescents, Child Abuse, Females, Height
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Ogasawara, Haruhiko – Structural Equation Modeling, 2001
Derives approximations to the distributions of goodness-of-fit indexes in structural equation modeling with the assumption of multivariate normality and slight misspecification of models. Also derives an approximation to the asymptotic covariance matrix for the fit indexes by using the delta method and develops approximations to the densities of…
Descriptors: Goodness of Fit, Statistical Distributions, Structural Equation Models
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Wen, Zhonglin; Marsh, Herbert W.; Hau, Kit-Tai – Structural Equation Modeling, 2002
Points out two concerns with recent research by F. Li and others (2000) and T. Duncan and others (1999) that extended the structural equation model of latent interactions developed by K. Joreskog and F. Yang (1996) to latent growth modeling. Used mathematical derivation and a comparison of alternative models fitted to simulated data to develop a…
Descriptors: Goodness of Fit, Interaction, Simulation, Structural Equation Models
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Raykov, Tenko – Applied Psychological Measurement, 1999
Suggests that modeling change on the latent dimensions of interest is a better approach to measuring change than focusing on observed change scores and their properties. Discusses a latent-variable modeling approach that focuses on ability-change scores to permit estimation of individual latent-change scores and the relationship of ability-change…
Descriptors: Ability, Change, Measurement Techniques, Models
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Raykov, Tenko – Applied Psychological Measurement, 1997
Describes a structural equation model that permits estimation of the reliability index and coefficient of a composite index for congeneric measures. The method is also helpful in exploring the factorial structure of an item set, and its use in scale reliability estimation and development is illustrated. (SLD)
Descriptors: Estimation (Mathematics), Reliability, Structural Equation Models, Test Construction
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Sigfusdottir, Inga-Dora; Farkas, George; Silver, Eric – Journal of Youth and Adolescence, 2004
Drawing on R. Agnew's (Foundation for a general strain theory of crime and delinquency. Criminology 30: 47-87, 1992) general strain theory, this paper examines whether depressed mood and anger mediate the effects of family conflict on delinquency. We examine data on 7,758 students, 14-16 years old, attending the compulsory 9th and 10th grades of…
Descriptors: Structural Equation Models, Delinquency, Conflict, Adolescents
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Biesanz, Jeremy C.; Deeb-Sossa, Natalia; Papadakis, Alison A.; Bollen, Kenneth A.; Curran, Patrick J. – Psychological Methods, 2004
The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of…
Descriptors: Intervals, Structural Equation Models, Computation, Regression (Statistics)
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Kosciulek, John F. – Rehabilitation Counseling Bulletin, 2005
One model that is potentially useful in the rehabilitation field is the Consumer-Directed Theory of Empowerment (CDTE; Kosciulek, 1999a). However, additional empirical data are needed to further develop and critically evaluate the CDTE. To accomplish this task, the purpose of this study was to test the hypothesized structural model CDTE in a…
Descriptors: Structural Equation Models, Vocational Rehabilitation, Databases, Longitudinal Studies
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Davey, Adam – Structural Equation Modeling: A Multidisciplinary Journal, 2005
Effects of incomplete data on fit indexes remain relatively unexplored. We evaluate a wide set of fit indexes (?[squared], root mean squared error of appproximation, Normed Fit Index [NFI], Tucker-Lewis Index, comparative fit index, gamma-hat, and McDonald's Centrality Index) varying conditions of sample size (100-1,000 in increments of 50),…
Descriptors: Goodness of Fit, Structural Equation Models, Data Analysis
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Graham, John W. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
Conventional wisdom in missing data research dictates adding variables to the missing data model when those variables are predictive of (a) missingness and (b) the variables containing missingness. However, it has recently been shown that adding variables that are correlated with variables containing missingness, whether or not they are related to…
Descriptors: Structural Equation Models, Simulation, Computation, Maximum Likelihood Statistics
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Kim, Kevin H. – Structural Equation Modeling, 2005
The relation among fit indexes, power, and sample size in structural equation modeling is examined. The noncentrality parameter is required to compute power. The 2 existing methods of computing power have estimated the noncentrality parameter by specifying an alternative hypothesis or alternative fit. These methods cannot be implemented easily and…
Descriptors: Structural Equation Models, Sample Size, Goodness of Fit
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McDonald, Roderick P. – Multivariate Behavioral Research, 2004
Conventional structural equation modeling fits a covariance structure implied by the equations of the model. This treatment of the model often gives misleading results because overall goodness of fit tests do not focus on the specific constraints implied by the model. An alternative treatment arising from Pearl's directed acyclic graph theory…
Descriptors: Equations (Mathematics), Goodness of Fit, Structural Equation Models
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