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Shi, Xin; Prevett, Pauline; Farnsworth, Valerie; Kwong, Koo Chun; Wan, Wanggen; He, Feng; Zhai, Qingqing; Zhen, Lu – Journal of Financial Counseling and Planning, 2019
This study develops a general method for modeling changes in response to items relating to students perceptions of personal finance and financial products. The new method is illustrated to analyze data from a sample of 1,250 students aged 16-18 who participated in a financial capability education study in the UK. We demonstrate how a quantitative…
Descriptors: Money Management, Literacy, Student Attitudes, Program Effectiveness
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Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A. – Journal of Educational and Behavioral Statistics, 2018
A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are…
Descriptors: Skill Development, Cognitive Measurement, Cognitive Processes, Markov Processes
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Del Giudice, Marco – Developmental Psychology, 2016
According to models of differential susceptibility, the same neurobiological and temperamental traits that determine increased sensitivity to stress and adversity also confer enhanced responsivity to the positive aspects of the environment. Differential susceptibility models have expanded to include complex developmental processes in which genetic…
Descriptors: Twins, Environmental Influences, Individual Development, Models
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Kohli, Nidhi; Koran, Jennifer; Henn, Lisa – Educational and Psychological Measurement, 2015
There are well-defined theoretical differences between the classical test theory (CTT) and item response theory (IRT) frameworks. It is understood that in the CTT framework, person and item statistics are test- and sample-dependent. This is not the perception with IRT. For this reason, the IRT framework is considered to be theoretically superior…
Descriptors: Test Theory, Item Response Theory, Factor Analysis, Models
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Li, Xiaomin; Wang, Wen-Chung – Journal of Educational Measurement, 2015
The assessment of differential item functioning (DIF) is routinely conducted to ensure test fairness and validity. Although many DIF assessment methods have been developed in the context of classical test theory and item response theory, they are not applicable for cognitive diagnosis models (CDMs), as the underlying latent attributes of CDMs are…
Descriptors: Test Bias, Models, Cognitive Measurement, Evaluation Methods
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Martin-Fernandez, Manuel; Revuelta, Javier – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
This study compares the performance of two estimation algorithms of new usage, the Metropolis-Hastings Robins-Monro (MHRM) and the Hamiltonian MCMC (HMC), with two consolidated algorithms in the psychometric literature, the marginal likelihood via EM algorithm (MML-EM) and the Markov chain Monte Carlo (MCMC), in the estimation of multidimensional…
Descriptors: Bayesian Statistics, Item Response Theory, Models, Comparative Analysis
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Lee, Soo; Suh, Youngsuk – Journal of Educational Measurement, 2018
Lord's Wald test for differential item functioning (DIF) has not been studied extensively in the context of the multidimensional item response theory (MIRT) framework. In this article, Lord's Wald test was implemented using two estimation approaches, marginal maximum likelihood estimation and Bayesian Markov chain Monte Carlo estimation, to detect…
Descriptors: Item Response Theory, Sample Size, Models, Error of Measurement
Feng, Junchen – ProQuest LLC, 2017
The future of education is human expertise and artificial intelligence working in conjunction, a revolution that will change the education as we know it. The Intelligent Tutoring System is a key component of this future. A quantitative measurement of efficacies of practice to heterogeneous learners is the cornerstone of building an effective…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Bayesian Statistics, Models
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Guyon, Hervé; Tensaout, Mouloud – Measurement: Interdisciplinary Research and Perspectives, 2016
In this article, the authors extend the results of Aguirre-Urreta, Rönkkö, and Marakas (2016) concerning the omission of a relevant causal indicator by testing the validity of the assumption that causal indicators are entirely superfluous to the measurement model and discuss the implications for measurement theory. Contrary to common wisdom…
Descriptors: Causal Models, Structural Equation Models, Formative Evaluation, Measurement
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Kelly, Sean; Ye, Feifei – Journal of Experimental Education, 2017
Educational analysts studying achievement and other educational outcomes frequently encounter an association between initial status and growth, which has important implications for the analysis of covariate effects, including group differences in growth. As explicated by Allison (1990), where only two time points of data are available, identifying…
Descriptors: Regression (Statistics), Models, Error of Measurement, Scores
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Depaoli, Sarah; Clifton, James P.; Cobb, Patrice R. – Journal of Educational and Behavioral Statistics, 2016
A review of the software Just Another Gibbs Sampler (JAGS) is provided. We cover aspects related to history and development and the elements a user needs to know to get started with the program, including (a) definition of the data, (b) definition of the model, (c) compilation of the model, and (d) initialization of the model. An example using a…
Descriptors: Monte Carlo Methods, Markov Processes, Computer Software, Models
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Dong, Yi; Biswas, Gautam – International Educational Data Mining Society, 2017
This paper discusses a novel approach for developing more refined and accurate learner models from student data collected from Open Ended Learning Environments (OELEs). OELEs provide students choice in how they go about constructing solutions to problems, and students exhibit a variety of learning behaviors in such environments. Building accurate…
Descriptors: Student Behavior, Models, Monte Carlo Methods, Learning Processes
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Meng, Xiang-Bin; Tao, Jian; Chang, Hua-Hua – Journal of Educational Measurement, 2015
The assumption of conditional independence between the responses and the response times (RTs) for a given person is common in RT modeling. However, when the speed of a test taker is not constant, this assumption will be violated. In this article we propose a conditional joint model for item responses and RTs, which incorporates a covariance…
Descriptors: Reaction Time, Test Items, Accuracy, Models
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Culpepper, Steven Andrew; Park, Trevor – Journal of Educational and Behavioral Statistics, 2017
A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…
Descriptors: Bayesian Statistics, Multivariate Analysis, Item Response Theory, Regression (Statistics)
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Schweig, Jonathan David; Pane, John F. – International Journal of Research & Method in Education, 2016
Demands for scientific knowledge of what works in educational policy and practice has driven interest in quantitative investigations of educational outcomes, and randomized controlled trials (RCTs) have proliferated under these conditions. In educational settings, even when individuals are randomized, both experimental and control students are…
Descriptors: Randomized Controlled Trials, Educational Research, Multivariate Analysis, Models
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