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Lamb, Kristen; Kettler, Todd – AERA Online Paper Repository, 2019
Imagination and creative self-efficacy (CSE) are important components of the creative process and outcomes but are rarely investigated together. To explore the relationship between personality factors, imaginative thinking, and CSE, survey responses were gathered from university students in a southwestern region in the United States (n = 1,731).…
Descriptors: Creativity, Self Efficacy, Personality Traits, Imagination
Hancock, Gregory R.; Johnson, Tessa – AERA Online Paper Repository, 2018
Longitudinal models provide researchers with a framework for investigating key aspects of change over time, but rarely is "time" itself modeled as a focal parameter of interest. Rather than treat time as purely an index of measurement occasions, the proposed Time to Criterion (T2C) growth model allows for modeling individual variability…
Descriptors: Statistical Analysis, Longitudinal Studies, Time, Structural Equation Models
Levy, Roy – AERA Online Paper Repository, 2017
A conceptual distinction is drawn between indicators, which serve to define latent variables, and outcomes, which do not. However, commonly used frequentist and Bayesian estimation procedures do not honor this distinction. They allow the outcomes to influence the latent variables and the measurement model parameters for the indicators, rendering…
Descriptors: Bayesian Statistics, Structural Equation Models, Sampling, Goodness of Fit
Lee, Kejin; Whittaker, Tiffany Ann – AERA Online Paper Repository, 2017
The latent growth model (LGM) in structural equation modeling (SEM) may be extended to allow for the modeling of associations among multiple latent growth trajectories, resulting in a multiple domain latent growth model (MDLGM). While the MDLGM is conceived as a more powerful multivariate analysis technique, the examination of its methodological…
Descriptors: Statistical Analysis, Growth Models, Structural Equation Models, Multivariate Analysis
Huang, Jiajing; Liang, Xinya; Yang, Yanyun – AERA Online Paper Repository, 2017
In Bayesian structural equation modeling (BSEM), prior settings may affect model fit, parameter estimation, and model comparison. This simulation study was to investigate how the priors impact evaluation of relative fit across competing models. The design factors for data generation included sample sizes, factor structures, data distributions, and…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Sample Size
Zigler, Christina K.; Ye, Feifei – AERA Online Paper Repository, 2016
Mediation in multi-level data can be examined using conflated multilevel modeling (CMM), unconflated multilevel modeling (UMM), or multilevel structural equation modeling (MSEM). A Monte Carlo study was performed to compare the three methods on bias, type I error, and power in a 1-1-1 model with random slopes. The three methods showed no…
Descriptors: Hierarchical Linear Modeling, Structural Equation Models, Monte Carlo Methods, Statistical Bias
Zhao, Yu; Lei, Pui-Wa – AERA Online Paper Repository, 2016
Despite the prevalence of ordinal observed variables in applied structural equation modeling (SEM) research, limited attention has been given to model evaluation methods suitable for ordinal variables, thus providing practitioners in the field with few guidelines to follow. This study represents a first attempt to thoroughly examine the…
Descriptors: Factor Analysis, Monte Carlo Methods, Causal Models, Least Squares Statistics
Cheung, Wai Ming; Huang, Yanli; Tsang, Hector W. H. – AERA Online Paper Repository, 2017
Hong Kong attained champion of the PIRLS 2011 that aroused keen interest in understanding the underlying reasons of a non-alphabetic language. The study aimed at unravelling various aspects of the student and home factors which contributed to remarkable Chinese reading performance. Totally 3,875 students from 132 primary schools completed the…
Descriptors: Chinese, Reading Achievement, Elementary School Students, Structural Equation Models