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Ruchkin, Vladislav; Gilliam, Walter S.; Mayes, Linda – Child Psychiatry and Human Development, 2008
In planning interventions it is essential to understand how adverse risk factors in early childhood are associated with child mental health problems, whether some types of problems can be better explained by the specific risk factors, and whether early risk factors are differently related to different types of child behavior problems. A community…
Descriptors: Behavior Problems, Structural Equation Models, Prevention, Drinking
Smith, Tracy D.; McMillan, Bradley F. – 2001
This paper reviews the theoretical background, optimal levels, strengths, weaknesses, and additional considerations of the most frequently used structural equation modeling (SEM) fit statistics in an effort to enable researchers to make better, more informative judgments regarding their models. Fit indices evaluate model fit for the data being…
Descriptors: Chi Square, Goodness of Fit, Structural Equation Models
Berkovits, Ilona; Hancock, Gregory R. – 2000
A simulation study compared three methods of estimating parameters within structural equation models (SEM) with polytomous variables. These methods appear in three SEM computer software packages: (1) LISREL (Joreskog and Sorbom, 1996) with PRELIS (Joreskog and Sorbom); (2) EQS (Bentler, 1995); and (3) the new Mplus (Muthen and Muthen, 1998). The…
Descriptors: Computer Software, Estimation (Mathematics), Simulation, Structural Equation Models

Coenders, Germa; Saris, Willem E.; Satorra, Albert – Structural Equation Modeling, 1997
A Monte Carlo study is reported that shows the comparative performance of alternative approaches under deviations from their respective assumptions in the case of structural equation models with latent variables with attention restricted to point estimates of model parameters. The conditional polychoric correlations method is shown most robust…
Descriptors: Estimation (Mathematics), Monte Carlo Methods, Structural Equation Models

Fan, Xitao – Structural Equation Modeling, 1997
The relationship between structural equation modeling (SEM) and canonical correlation analysis (CCA) is illustrated. The representation of CCA in SEM may provide interpretive information not available from conventional CCA. Hierarchically, the relationship suggests that SEM is a more general analytic approach. (SLD)
Descriptors: Correlation, Research Methodology, Statistical Analysis, Structural Equation Models

Shipley, Bill – Structural Equation Modeling, 2003
Shows how to extend the inferential test of B. Shipley (2000), which is applicable to recursive path models without correlated errors, to a class of recursive path models that includes correlated errors. Discusses when the extended model is and is not superior to classical structural equation modeling. (SLD)
Descriptors: Correlation, Path Analysis, Statistical Inference, Structural Equation Models

Reinartz, Werner J.; Echambadi, Raj; Cin, Wynne W. – Multivariate Behavioral Research, 2002
Tested empirically the applicability of a method developed by S. Mattson for generating data on latent variables with controlled skewness and kurtosis of the observed variables. Monte Carlo simulation results suggest that Mattson's method appears to be a good approach to generate data with defined levels of skewness and kurtosis. (SLD)
Descriptors: Computer Simulation, Monte Carlo Methods, Structural Equation Models

Raykov, Tenko; Penev, Spiridon – Structural Equation Modeling, 1998
Discusses the difference in noncentrality parameters of nested structural equation models and their utility in evaluating statistical power associated with the pertinent restriction test. Asymptotic confidence intervals for that difference are presented. These intervals represent a useful adjunct to goodness-of-fit indexes in assessing constraints…
Descriptors: Goodness of Fit, Power (Statistics), Structural Equation Models

Bollen, Kenneth A.; Paxton, Pamela – Structural Equation Modeling, 1998
Provides a discussion of an alternative two-stage least squares (2SLS) technique to include interactions of latent variables in structural equation models. The method requires selection of instrumental variables, and rules for selection are presented. An empirical example and Statistical Analysis System programs are presented. (SLD)
Descriptors: Interaction, Least Squares Statistics, Selection, Structural Equation Models

Scheines, Richard; Hoijtink, Herbert; Boomsma, Anne – Psychometrika, 1999
Explains how the Gibbs sampler can be applied to obtain a sample from the posterior distribution over the parameters of a structural equation model. Presents statistics to use to summarize marginal posterior densities and model checks using posterior predictive p-values. (SLD)
Descriptors: Bayesian Statistics, Estimation (Mathematics), Sampling, Structural Equation Models

Raykov, Tenko; Marcoulides, George A. – Structural Equation Modeling, 2000
Outlines a method for comparing completely standardized solutions in multiple groups. The method is based on a correlation structure analysis of equal-size samples and uses the correlation distribution theory implemented in the structural equation modeling program RAMONA. (SLD)
Descriptors: Comparative Analysis, Correlation, Sample Size, Structural Equation Models

Yuan, Ke-Hai; Bentler, Peter M. – Psychometrika, 2000
Adapts robust schemes to mean and covariance structures, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is weighted according to its distance, based on first and second order moments. Test statistics and standard error estimators are given. (SLD)
Descriptors: Least Squares Statistics, Robustness (Statistics), Structural Equation Models

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

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

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