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Geiser, Christian; Eid, Michael; West, Stephen G.; Lischetzke, Tanja; Nussbeck, Fridtjof W. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Multimethod data analysis is a complex procedure that is often used to examine the degree to which different measures of the same construct converge in the assessment of this construct. Several authors have called for a greater understanding of the definition and meaning of method effects in different models for multimethod data. In this article,…
Descriptors: Structural Equation Models, Factor Analysis, Multitrait Multimethod Techniques, Comparative Analysis
Peugh, James L.; Enders, Craig K. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Cluster sampling results in response variable variation both among respondents (i.e., within-cluster or Level 1) and among clusters (i.e., between-cluster or Level 2). Properly modeling within- and between-cluster variation could be of substantive interest in numerous settings, but applied researchers typically test only within-cluster (i.e.,…
Descriptors: Structural Equation Models, Monte Carlo Methods, Multivariate Analysis, Sampling
Bandalos, Deborah L. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This study examined the efficacy of 4 different parceling methods for modeling categorical data with 2, 3, and 4 categories and with normal, moderately nonnormal, and severely nonnormal distributions. The parceling methods investigated were isolated parceling in which items were parceled with other items sharing the same source of variance, and…
Descriptors: Structural Equation Models, Computation, Goodness of Fit, Classification
Herzog, Walter; Boomsma, Anne; Reinecke, Sven – Structural Equation Modeling: A Multidisciplinary Journal, 2007
According to Kenny and McCoach (2003), chi-square tests of structural equation models produce inflated Type I error rates when the degrees of freedom increase. So far, the amount of this bias in large models has not been quantified. In a Monte Carlo study of confirmatory factor models with a range of 48 to 960 degrees of freedom it was found that…
Descriptors: Monte Carlo Methods, Structural Equation Models, Effect Size, Maximum Likelihood Statistics

Raykov, Tenko – Applied Psychological Measurement, 1998
Examines the relationship between Cronbach's coefficient alpha and the reliability of a composite of a prespecified set of interrelated nonhomogeneous components through simulation. Shows that alpha can over- or underestimate scale reliability at the population level. Illustrates the bias in terms of structural parameters. (SLD)
Descriptors: Reliability, Simulation, Statistical Bias, Structural Equation Models
Wang, Zhongmiao; Thompson, Bruce – Journal of Experimental Education, 2007
In this study the authors investigated the use of 5 (i.e., Claudy, Ezekiel, Olkin-Pratt, Pratt, and Smith) R[squared] correction formulas with the Pearson r[squared]. The authors estimated adjustment bias and precision under 6 x 3 x 6 conditions (i.e., population [rho] values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9; population shapes normal, skewness…
Descriptors: Effect Size, Correlation, Mathematical Formulas, Monte Carlo Methods

Robles, Jaime – Structural Equation Modeling, 1996
A theoretical and philosophical revision of the concept of fit in structural equation modeling and its relation to a confirmation bias is developed. The neutral character of fit indexes regarding this issue is argued, concluding that protection against confirmation bias relies on model modification strategy and scientist behavior. (SLD)
Descriptors: Causal Models, Goodness of Fit, Mathematical Models, Statistical Bias
Thompson, Bruce; Fan, Xitao – 1998
This study empirically investigated bootstrap bias estimation in the area of structural equation modeling (SEM). Three correctly specified SEM models were used under four different sample size conditions. Monte Carlo experiments were carried out to generate the criteria against which bootstrap bias estimation should be judged. For SEM fit indices,…
Descriptors: Estimation (Mathematics), Goodness of Fit, Monte Carlo Methods, Sample Size
Fan, Xitao; And Others – 1997
A Monte Carlo study was conducted to assess the effects of some potential confounding factors on structural equation modeling (SEM) fit indices and parameter estimates for both true and misspecified models. The factors investigated were data nonnormality, SEM estimation method, and sample size. Based on the fully crossed and balanced 3x3x4x2…
Descriptors: Estimation (Mathematics), Goodness of Fit, Mathematical Models, Monte Carlo Methods
Jo, See-Heyon – 1995
The question of how to analyze unbalanced hierarchical data generated from structural equation models has been a common problem for researchers and analysts. Among difficulties plaguing statistical modeling are estimation bias due to measurement error and the estimation of the effects of the individual's hierarchical social milieu. This paper…
Descriptors: Algorithms, Bayesian Statistics, Equations (Mathematics), Error of Measurement

Wu, Ping; Campbell, Donald T. – Evaluation and Program Planning, 1996
Structural equation models reanalyzed data from the 1969 Westinghouse Head Start evaluation and extended the analysis to blacks and full-year programs. Results showed that the selection bias for summer program whites was absent for blacks and full-year program whites, and that Head Start helped the most disadvantaged children. (SLD)
Descriptors: Black Students, Data Analysis, Disadvantaged Youth, Early Childhood Education
Mayer, Matthew J. – Journal of School Violence, 2004
Methodological challenges associated with structural equation modeling (SEM) and structured means modeling (SMM) in research on school violence and related topics in the social and behavioral sciences are examined. Problems associated with multiyear implementations of large-scale surveys are discussed. Complex sample designs, part of any…
Descriptors: Violence, Structural Equation Models, Research Methodology, Measures (Individuals)