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Kazuki Hori – ProQuest LLC, 2021
Educational researchers are often interested in phenomena that unfold over time within a person and at the same time, relationships between their characteristics that are stable over time. Since variables in a longitudinal study reflect both within- and between-person effects, researchers need to disaggregate them to understand the phenomenon of…
Descriptors: Time, Structural Equation Models, Monte Carlo Methods, Simulation
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Baker, David Scott; Underwood, James, III; Thakur, Ramendra – Marketing Education Review, 2017
This study aimed to establish a pedagogical positioning of a business marketing simulation as a grounded learning teaching tool and empirically assess the dimensions of cognitive absorption related to grounded learning effectiveness in an iterative business simulation environment. The method/design and sample consisted of a field study survey…
Descriptors: Marketing, Simulation, Predictor Variables, Instructional Effectiveness
Kern, Justin L.; McBride, Brent A.; Laxman, Daniel J.; Dyer, W. Justin; Santos, Rosa M.; Jeans, Laurie M. – Grantee Submission, 2016
Measurement invariance (MI) is a property of measurement that is often implicitly assumed, but in many cases, not tested. When the assumption of MI is tested, it generally involves determining if the measurement holds longitudinally or cross-culturally. A growing literature shows that other groupings can, and should, be considered as well.…
Descriptors: Psychology, Measurement, Error of Measurement, Measurement Objectives
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Aydin, Burak; Leite, Walter L.; Algina, James – Educational and Psychological Measurement, 2016
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…
Descriptors: Error of Measurement, Predictor Variables, Randomized Controlled Trials, Experimental Groups
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Kelava, Augustin; Nagengast, Benjamin – Multivariate Behavioral Research, 2012
Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we present a Bayesian model for the estimation of latent nonlinear effects when the latent…
Descriptors: Bayesian Statistics, Computation, Structural Equation Models, Predictor Variables
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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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Kelava, Augustin; Werner, Christina S.; Schermelleh-Engel, Karin; Moosbrugger, Helfried; Zapf, Dieter; Ma, Yue; Cham, Heining; Aiken, Leona S.; West, Stephen G. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x[superscript 2] [subscript 1], x[subscript 1]x[subscript 4]) to serve as indicators of each nonlinear latent construct. These approaches require the use of…
Descriptors: Simulation, Computation, Evaluation, Predictor Variables
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Williams, Jason; MacKinnon, David P. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Recent advances in testing mediation have found that certain resampling methods and tests based on the mathematical distribution of 2 normal random variables substantially outperform the traditional "z" test. However, these studies have primarily focused only on models with a single mediator and 2 component paths. To address this limitation, a…
Descriptors: Intervals, Testing, Predictor Variables, Effect Size
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Buhner, Markus; Kroner, Stephan; Ziegler, Matthias – Intelligence, 2008
The relationship between working memory, intelligence and problem-solving is explored. Wittmann and Suss [Wittmann, W.W., & Suss, H.M. (1999). Investigating the paths between working memory, intelligence, knowledge, and complex problem-solving performances via Brunswik symmetry. In P.L. Ackerman, R.D. Roberts (Ed.), "Learning and individual…
Descriptors: Undergraduate Students, Intelligence, Problem Solving, Short Term Memory
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Ogasawara, Haruhiko – Psychometrika, 2004
Formulas for the asymptotic biases of the parameter estimates in structural equation models are provided in the case of the Wishart maximum likelihood estimation for normally and nonnormally distributed variables. When multivariate normality is satisfied, considerable simplification is obtained for the models of unstandardized variables. Formulas…
Descriptors: Evaluation Methods, Bias, Factor Analysis, Structural Equation Models
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Bai, Yun; Poon, Wai-Yin; Cheung, Gordon Wai Hung – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Two-level data sets are frequently encountered in social and behavioral science research. They arise when observations are drawn from a known hierarchical structure, as when individuals are randomly drawn from groups that are randomly drawn from a target population. When the covariance structures in the group level and the individual level are the…
Descriptors: Evaluation Methods, Predictor Variables, Social Science Research, Behavioral Science Research