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Nuria Real-Brioso; Eduardo Estrada; Pablo F. Cáncer – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Accelerated longitudinal designs (ALDs) provide an opportunity to capture long developmental periods in a shorter time framework using a relatively small number of assessments. Prior literature has investigated whether univariate developmental processes can be characterized with data obtained from ALDs. However, many important questions in…
Descriptors: Longitudinal Studies, Psychology, Cognitive Development, Brain Hemisphere Functions
Hildreth, Laura A.; Genschel, Ulrike; Lorenz, Frederick O.; Lesser, Virginia M. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Response patterns are of importance to survey researchers because of the insight they provide into the thought processes respondents use to answer survey questions. In this article we propose the use of structural equation modeling to examine response patterns and develop a permutation test to quantify the likelihood of observing a specific…
Descriptors: Questionnaires, Response Style (Tests), Structural Equation Models, Surveys
Tomas, Jose M.; Oliver, Amparo; Galiana, Laura; Sancho, Patricia; Lila, Marisol – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Several investigators have interpreted method effects associated with negatively worded items in a substantive way. This research extends those studies in different ways: (a) it establishes the presence of methods effects in further populations and particular scales, and (b) it examines the possible relations between a method factor associated…
Descriptors: Correlation, Self Esteem, Measures (Individuals), High School Students
Wu, Jiun-Yu; Kwok, Oi-man – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Both ad-hoc robust sandwich standard error estimators (design-based approach) and multilevel analysis (model-based approach) are commonly used for analyzing complex survey data with nonindependent observations. Although these 2 approaches perform equally well on analyzing complex survey data with equal between- and within-level model structures…
Descriptors: Structural Equation Models, Surveys, Data Analysis, Comparative Analysis
Raykov, Tenko; Marcoulides, George A. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
A directly applicable latent variable modeling procedure for classical item analysis is outlined. The method allows one to point and interval estimate item difficulty, item correlations, and item-total correlations for composites consisting of categorical items. The approach is readily employed in empirical research and as a by-product permits…
Descriptors: Item Analysis, Evaluation, Correlation, Test Items
Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…
Descriptors: Sample Size, Simulation, Factor Structure, Statistical Analysis
DeMars, Christine E. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
In structural equation modeling software, either limited-information (bivariate proportions) or full-information item parameter estimation routines could be used for the 2-parameter item response theory (IRT) model. Limited-information methods assume the continuous variable underlying an item response is normally distributed. For skewed and…
Descriptors: Item Response Theory, Structural Equation Models, Computation, Computer Software
Leite, Walter L.; Zuo, Youzhen – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Among the many methods currently available for estimating latent variable interactions, the unconstrained approach is attractive to applied researchers because of its relatively easy implementation with any structural equation modeling (SEM) software. Using a Monte Carlo simulation study, we extended and evaluated the unconstrained approach to…
Descriptors: Monte Carlo Methods, Structural Equation Models, Evaluation, Researchers
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
Cheung, Mike W. -L. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Confidence intervals (CIs) for parameters are usually constructed based on the estimated standard errors. These are known as Wald CIs. This article argues that likelihood-based CIs (CIs based on likelihood ratio statistics) are often preferred to Wald CIs. It shows how the likelihood-based CIs and the Wald CIs for many statistics and psychometric…
Descriptors: Intervals, Structural Equation Models, Simulation, Correlation
Cheung, Mike W. L.; Chan, Wai – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Structural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation…
Descriptors: Structural Equation Models, Simulation, Social Sciences, Correlation
Marsh, Herbert W.; Ludtke, Oliver; Trautwein, Ulrich; Morin, Alexandre J. S. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing…
Descriptors: Structural Equation Models, Goodness of Fit, Profiles, Prediction
van der Sluis, Sophie; Dolan, Conor V.; Stoel, Reinoud D. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This article is concerned with the seemingly simple problem of testing whether latent factors are perfectly correlated (i.e., statistically indistinct). In recent literature, researchers have used different approaches, which are not always correct or complete. We discuss the parameter constraints required to obtain such perfectly correlated latent…
Descriptors: Testing, Factor Structure, Structural Equation Models, Correlation
Beretvas, S. Natasha; Furlow, Carolyn F. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Meta-analytic structural equation modeling (MA-SEM) is increasingly being used to assess model-fit for variables' interrelations synthesized across studies. MA-SEM researchers have analyzed synthesized correlation matrices using structural equation modeling (SEM) estimation that is designed for covariance matrices. This can produce incorrect…
Descriptors: Structural Equation Models, Matrices, Statistical Analysis, Synthesis

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