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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
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Pek, Jolynn; Losardo, Diane; Bauer, Daniel J. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…
Descriptors: Structural Equation Models, Sampling, Statistical Inference, Computation
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Finch, W. Holmes; French, Brian F. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
The purpose of this simulation study was to assess the performance of latent variable models that take into account the complex sampling mechanism that often underlies data used in educational, psychological, and other social science research. Analyses were conducted using the multiple indicator multiple cause (MIMIC) model, which is a flexible…
Descriptors: Causal Models, Computation, Data, Sampling
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Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…
Descriptors: Structural Equation Models, Mixed Methods Research, Statistical Analysis, Sampling
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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
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Stapleton, Laura M. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This article discusses replication sampling variance estimation techniques that are often applied in analyses using data from complex sampling designs: jackknife repeated replication, balanced repeated replication, and bootstrapping. These techniques are used with traditional analyses such as regression, but are currently not used with structural…
Descriptors: Structural Equation Models, Simulation, Sampling, Longitudinal Studies