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Maeda, Hotaka; Zhang, Bo – International Journal of Testing, 2017
The omega (?) statistic is reputed to be one of the best indices for detecting answer copying on multiple choice tests, but its performance relies on the accurate estimation of copier ability, which is challenging because responses from the copiers may have been contaminated. We propose an algorithm that aims to identify and delete the suspected…
Descriptors: Cheating, Test Items, Mathematics, Statistics
Culpepper, Steven Andrew – Journal of Educational and Behavioral Statistics, 2015
A Bayesian model formulation of the deterministic inputs, noisy "and" gate (DINA) model is presented. Gibbs sampling is employed to simulate from the joint posterior distribution of item guessing and slipping parameters, subject attribute parameters, and latent class probabilities. The procedure extends concepts in Béguin and Glas,…
Descriptors: Bayesian Statistics, Models, Sampling, Computation
Guastella, Ivan; Fazio, Claudio; Sperandeo-Mineo, Rosa Maria – European Journal of Physics, 2012
A procedure modelling ideal classical and quantum gases is discussed. The proposed approach is mainly based on the idea that modelling and algorithm analysis can provide a deeper understanding of particularly complex physical systems. Appropriate representations and physical models able to mimic possible pseudo-mechanisms of functioning and having…
Descriptors: Predictive Validity, Quantum Mechanics, Science Education, Science Instruction
Sanborn, Adam N.; Griffiths, Thomas L.; Navarro, Daniel J. – Psychological Review, 2010
Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models…
Descriptors: Models, Cognitive Processes, Psychology, Monte Carlo Methods
Kim, Jee-Seon; Bolt, Daniel M. – Educational Measurement: Issues and Practice, 2007
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…
Descriptors: Placement, Monte Carlo Methods, Markov Processes, Measurement
Maximum Likelihood Analysis of a Two-Level Nonlinear Structural Equation Model with Fixed Covariates
Lee, Sik-Yum; Song, Xin-Yuan – Journal of Educational and Behavioral Statistics, 2005
In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects…
Descriptors: Mathematics, Sampling, Structural Equation Models, Bayesian Statistics