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Hongxi Li; Shuwei Li; Liuquan Sun; Xinyuan Song – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social,…
Descriptors: Statistical Analysis, Statistical Studies, Structural Equation Models, Intervals
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Holmes, Christopher; Kim-Spoon, Jungmeen – Journal of Early Adolescence, 2017
Although religiousness has been identified as a protective factor against adolescent substance use, processes through which these effects may operate are unclear. The current longitudinal study examined sequential mediation of afterlife beliefs and future orientation in the relation between adolescent religiousness and cigarette, alcohol, and…
Descriptors: Religion, Beliefs, Role, Correlation
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Sideridis, Georgios; Simos, Panagiotis; Papanicolaou, Andrew; Fletcher, Jack – Educational and Psychological Measurement, 2014
The present study assessed the impact of sample size on the power and fit of structural equation modeling applied to functional brain connectivity hypotheses. The data consisted of time-constrained minimum norm estimates of regional brain activity during performance of a reading task obtained with magnetoencephalography. Power analysis was first…
Descriptors: Structural Equation Models, Brain Hemisphere Functions, Simulation, Models
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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
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Herzog, Walter; Boomsma, Anne – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Traditional estimators of fit measures based on the noncentral chi-square distribution (root mean square error of approximation [RMSEA], Steiger's [gamma], etc.) tend to overreject acceptable models when the sample size is small. To handle this problem, it is proposed to employ Bartlett's (1950), Yuan's (2005), or Swain's (1975) correction of the…
Descriptors: Intervals, Sample Size, Monte Carlo Methods, Computation
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Ecker, Ullrich K. H.; Lewandowsky, Stephan; Oberauer, Klaus; Chee, Abby E. H. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2010
Working memory updating (WMU) has been identified as a cognitive function of prime importance for everyday tasks and has also been found to be a significant predictor of higher mental abilities. Yet, little is known about the constituent processes of WMU. We suggest that operations required in a typical WMU task can be decomposed into 3 major…
Descriptors: Structural Equation Models, Short Term Memory, Cognitive Processes, Cognitive Ability
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Krishnakumar, Jaya; Nagar, A. L. – Social Indicators Research, 2008
Recent empirical literature has seen many multidimensional indices emerge as well-being or poverty measures, in particular indices derived from principal components and various latent variable models. Though such indices are being increasingly and widely employed, few studies motivate their use or report the standard errors or confidence intervals…
Descriptors: Intervals, Structural Equation Models, Factor Analysis, Computation
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Raykov, Tenko; Marcoulides, George A. – Structural Equation Modeling, 2004
In applications of structural equation modeling, it is often desirable to obtain measures of uncertainty for special functions of model parameters. This article provides a didactic discussion of how a method widely used in applied statistics can be employed for approximate standard error and confidence interval evaluation of such functions. The…
Descriptors: Intervals, Structural Equation Models, Evaluation Methods, Statistical Analysis
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Klein, Andreas G.; Muthen, Bengt O. – Journal of Educational and Behavioral Statistics, 2006
In this article, a heterogeneous latent growth curve model for modeling heterogeneity of growth rates is proposed. The suggested model is an extension of a conventional growth curve model and a complementary tool to mixed growth modeling. It allows the modeling of heterogeneity of growth rates as a continuous function of latent initial status and…
Descriptors: Intervals, Computation, Structural Equation Models, Mathematics Achievement