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Mair, Patrick; Satorra, Albert; Bentler, Peter M. – Multivariate Behavioral Research, 2012
This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo…
Descriptors: Structural Equation Models, Data, Monte Carlo Methods, Probability
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Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S. – Multivariate Behavioral Research, 2012
A Monte Carlo simulation was conducted to investigate the robustness of 4 latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of nonnormality of the observed…
Descriptors: Monte Carlo Methods, Computation, Robustness (Statistics), Structural Equation Models
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Biesanz, Jeremy C.; Falk, Carl F.; Savalei, Victoria – Multivariate Behavioral Research, 2010
Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses (Baron & Kenny, 1986; Sobel, 1982) have in recent years…
Descriptors: Computation, Intervals, Models, Monte Carlo Methods
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Hung, Lai-Fa – Multivariate Behavioral Research, 2010
Longitudinal data describe developmental patterns and enable predictions of individual changes beyond sampled time points. Major methodological issues in longitudinal data include modeling random effects, subject effects, growth curve parameters, and autoregressive residuals. This study embedded the longitudinal model within a multigroup…
Descriptors: Longitudinal Studies, Data, Models, Markov Processes
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Chun, So Yeon; Shapiro, Alexander – Multivariate Behavioral Research, 2009
The noncentral chi-square approximation of the distribution of the likelihood ratio (LR) test statistic is a critical part of the methodology in structural equation modeling. Recently, it was argued by some authors that in certain situations normal distributions may give a better approximation of the distribution of the LR test statistic. The main…
Descriptors: Statistical Analysis, Structural Equation Models, Validity, Monte Carlo Methods
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Hsieh, Chueh-An; von Eye, Alexander A.; Maier, Kimberly S. – Multivariate Behavioral Research, 2010
The application of multidimensional item response theory models to repeated observations has demonstrated great promise in developmental research. It allows researchers to take into consideration both the characteristics of item response and measurement error in longitudinal trajectory analysis, which improves the reliability and validity of the…
Descriptors: Item Response Theory, Change, Adolescents, Social Isolation
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Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas – Multivariate Behavioral Research, 2011
The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…
Descriptors: Monte Carlo Methods, Patients, Probability, Item Response Theory
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Stadnytska, Tetiana; Braun, Simone; Werner, Joachim – Multivariate Behavioral Research, 2008
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Descriptors: Models, Identification, Multivariate Analysis, Correlation
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Wanstrom, Linda – Multivariate Behavioral Research, 2009
Second-order latent growth curve models (S. C. Duncan & Duncan, 1996; McArdle, 1988) can be used to study group differences in change in latent constructs. We give exact formulas for the covariance matrix of the parameter estimates and an algebraic expression for the estimation of slope differences. Formulas for calculations of the required sample…
Descriptors: Sample Size, Effect Size, Mathematical Formulas, Computation
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Dinno, Alexis – Multivariate Behavioral Research, 2009
Horn's parallel analysis (PA) is the method of consensus in the literature on empirical methods for deciding how many components/factors to retain. Different authors have proposed various implementations of PA. Horn's seminal 1965 article, a 1996 article by Thompson and Daniel, and a 2004 article by Hayton, Allen, and Scarpello all make assertions…
Descriptors: Structural Equation Models, Item Response Theory, Computer Software, Surveys
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Savalei, Victoria; Yuan, Ke-Hai – Multivariate Behavioral Research, 2009
Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) for general covariance structure models and applied to structural equation modeling by Bollen and Stine…
Descriptors: Statistical Inference, Goodness of Fit, Structural Equation Models, Transformations (Mathematics)
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Wang, Lijuan; Zhang, Zhiyong; McArdle, John J.; Salthouse, Timothy A. – Multivariate Behavioral Research, 2008
Score limitation at the top of a scale is commonly termed "ceiling effect." Ceiling effects can lead to serious artifactual parameter estimates in most data analysis. This study examines the consequences of ceiling effects in longitudinal data analysis and investigates several methods of dealing with ceiling effects through Monte Carlo simulations…
Descriptors: Longitudinal Studies, Data Analysis, Evaluation Methods, Monte Carlo Methods
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Fan, Xitao; Sivo, Stephen A. – Multivariate Behavioral Research, 2007
The search for cut-off criteria of fit indices for model fit evaluation (e.g., Hu & Bentler, 1999) assumes that these fit indices are sensitive to model misspecification, but not to different types of models. If fit indices were sensitive to different types of models that are misspecified to the same degree, it would be very difficult to establish…
Descriptors: Structural Equation Models, Criteria, Monte Carlo Methods, Factor Analysis
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Curran, Patrick J.; Bollen, Kenneth A.; Paxton, Pamela; Kirby, James; Chen, Feinian – Multivariate Behavioral Research, 2002
Examined several hypotheses about the suitability of the noncentral chi square in applied research using Monte Carlo simulation experiments with seven sample sizes and three distinct model types, each with five specifications. Results show that, in general, for models with small to moderate misspecification, the noncentral chi-square is well…
Descriptors: Chi Square, Models, Monte Carlo Methods, Sample Size
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Song, Xin-Yuan; Lee, Sik-Yum – Multivariate Behavioral Research, 2006
In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…
Descriptors: Structural Equation Models, Bayesian Statistics, Markov Processes, Monte Carlo Methods
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