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A. M. Sadek; Fahad Al-Muhlaki – Measurement: Interdisciplinary Research and Perspectives, 2024
In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain…
Descriptors: Monte Carlo Methods, Accuracy, Artificial Intelligence, Guidelines
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Shu, Tian; Luo, Guanzhong; Luo, Zhaosheng; Yu, Xiaofeng; Guo, Xiaojun; Li, Yujun – Journal of Educational and Behavioral Statistics, 2023
Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects' latent attributes are dichotomous--mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a…
Descriptors: Cognitive Measurement, Models, Diagnostic Tests, Accuracy
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Lee, Daniel Y.; Harring, Jeffrey R. – Journal of Educational and Behavioral Statistics, 2023
A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation-maximization algorithm, (c) multiple imputation, (d) a two-stage multiple…
Descriptors: Monte Carlo Methods, Research Problems, Statistical Inference, Bayesian Statistics
Yao, Yuling; Vehtari, Aki; Gelman, Andrew – Grantee Submission, 2022
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in…
Descriptors: Bayesian Statistics, Computation, Markov Processes, Monte Carlo Methods
Batley, Prathiba Natesan; Minka, Tom; Hedges, Larry Vernon – Grantee Submission, 2020
Immediacy is one of the necessary criteria to show strong evidence of treatment effect in single case experimental designs (SCEDs). With the exception of Natesan and Hedges (2017) no inferential statistical tool has been used to demonstrate or quantify it until now. We investigate and quantify immediacy by treating the change-points between the…
Descriptors: Bayesian Statistics, Monte Carlo Methods, Statistical Inference, Markov Processes
Natesan, Prathiba; Hedges, Larry V. – Grantee Submission, 2016
Although immediacy is one of the necessary criteria to show strong evidence of a causal relation in SCDs, no inferential statistical tool is currently used to demonstrate it. We propose a Bayesian unknown change-point model to investigate and quantify immediacy in SCD analysis. Unlike visual analysis that considers only 3-5 observations in…
Descriptors: Bayesian Statistics, Statistical Inference, Research Design, Models
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Kim, Seock-Ho; And Others – Applied Psychological Measurement, 1994
Type I error rates of F. M. Lord's chi square test for differential item functioning were investigated using Monte Carlo simulations with marginal maximum likelihood estimation and marginal Bayesian estimation algorithms. Lord's chi square did not provide useful Type I error control for the three-parameter logistic model at these sample sizes.…
Descriptors: Algorithms, Bayesian Statistics, Chi Square, Error of Measurement