Publication Date
In 2025 | 3 |
Since 2024 | 8 |
Since 2021 (last 5 years) | 18 |
Since 2016 (last 10 years) | 50 |
Since 2006 (last 20 years) | 113 |
Descriptor
Bayesian Statistics | 123 |
Models | 123 |
Simulation | 110 |
Computation | 39 |
Item Response Theory | 38 |
Comparative Analysis | 32 |
Probability | 23 |
Evaluation Methods | 22 |
Monte Carlo Methods | 21 |
Markov Processes | 20 |
Data Analysis | 19 |
More ▼ |
Source
Author
Almond, Russell G. | 3 |
Cohen, Allan S. | 3 |
Mulder, Joris | 3 |
Wagenmakers, Eric-Jan | 3 |
Zhiyong Zhang | 3 |
Barnes, Tiffany, Ed. | 2 |
Cho, Sun-Joo | 2 |
Chu, Haitao | 2 |
Fujimoto, Ken A. | 2 |
Glas, Cees A. W. | 2 |
Gobert, Janice | 2 |
More ▼ |
Publication Type
Education Level
Secondary Education | 8 |
Higher Education | 6 |
Junior High Schools | 6 |
Middle Schools | 6 |
Elementary Education | 4 |
Postsecondary Education | 4 |
Adult Education | 3 |
Grade 8 | 3 |
Grade 3 | 2 |
High Schools | 2 |
Early Childhood Education | 1 |
More ▼ |
Audience
Researchers | 3 |
Location
Massachusetts | 3 |
Spain | 3 |
Australia | 2 |
Netherlands | 2 |
North Carolina | 2 |
Belgium | 1 |
China | 1 |
Czech Republic | 1 |
Florida | 1 |
Florida (Miami) | 1 |
France | 1 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Jihong Zhang; Jonathan Templin; Xinya Liang – Journal of Educational Measurement, 2024
Recently, Bayesian diagnostic classification modeling has been becoming popular in health psychology, education, and sociology. Typically information criteria are used for model selection when researchers want to choose the best model among alternative models. In Bayesian estimation, posterior predictive checking is a flexible Bayesian model…
Descriptors: Bayesian Statistics, Cognitive Measurement, Models, Classification
Jean-Paul Fox – Journal of Educational and Behavioral Statistics, 2025
Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…
Descriptors: Sample Size, Item Response Theory, Accuracy, Bayesian Statistics
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
Jansen, Katrin; Holling, Heinz – Research Synthesis Methods, 2023
In meta-analyses of rare events, it can be challenging to obtain a reliable estimate of the pooled effect, in particular when the meta-analysis is based on a small number of studies. Recent simulation studies have shown that the beta-binomial model is a promising candidate in this situation, but have thus far only investigated its performance in a…
Descriptors: Bayesian Statistics, Meta Analysis, Probability, Simulation
Kazuhiro Yamaguchi – Journal of Educational and Behavioral Statistics, 2025
This study proposes a Bayesian method for diagnostic classification models (DCMs) for a partially known Q-matrix setting between exploratory and confirmatory DCMs. This Q-matrix setting is practical and useful because test experts have pre-knowledge of the Q-matrix but cannot readily specify it completely. The proposed method employs priors for…
Descriptors: Models, Classification, Bayesian Statistics, Evaluation Methods
Jihong Zhang – ProQuest LLC, 2022
Recently, Bayesian diagnostic classification modeling has been becoming popular in health psychology, education, and sociology. Typically information criteria are used for model selection when researchers want to choose the best model among alternative models. In Bayesian estimation, posterior predictive checking is a flexible Bayesian model…
Descriptors: Bayesian Statistics, Cognitive Measurement, Models, Classification
Jennifer L. Proper; Haitao Chu; Purvi Prajapati; Michael D. Sonksen; Thomas A. Murray – Research Synthesis Methods, 2024
Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not…
Descriptors: Network Analysis, Meta Analysis, Prediction, Drug Therapy
Saijun Zhao; Zhiyong Zhang; Hong Zhang – Grantee Submission, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
Saijun Zhao; Zhiyong Zhang; Hong Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time,…
Descriptors: Bayesian Statistics, Statistical Inference, Longitudinal Studies, Attribution Theory
Sonu Jose – ProQuest LLC, 2020
Bayesian network is a probabilistic graphical model that has wide applications in various domains due to its peculiarity of knowledge representation and reasoning under uncertainty. This research aims at Bayesian network structure learning and how the learned model can be used for reasoning. Learning the structure of Bayesian network from data is…
Descriptors: Bayesian Statistics, Models, Simulation, Algorithms
Zhenqiu Lu; Zhiyong Zhang – Grantee Submission, 2022
Bayesian approach is becoming increasingly important as it provides many advantages in dealing with complex data. However, there is no well-defined model selection criterion or index in a Bayesian context. To address the challenges, new indices are needed. The goal of this study is to propose new model selection indices and to investigate their…
Descriptors: Models, Goodness of Fit, Bayesian Statistics, Simulation
Siegel, Lianne; Chu, Haitao – Research Synthesis Methods, 2023
Reference intervals, or reference ranges, aid medical decision-making by containing a pre-specified proportion (e.g., 95%) of the measurements in a representative healthy population. We recently proposed three approaches for estimating a reference interval from a meta-analysis based on a random effects model: a frequentist approach, a Bayesian…
Descriptors: Bayesian Statistics, Meta Analysis, Intervals, Decision Making
Fay, Derek M.; Levy, Roy; Schulte, Ann C. – Journal of Experimental Education, 2022
Longitudinal data structures are frequently encountered in a variety of disciplines in the social and behavioral sciences. Growth curve modeling offers a highly extensible framework that allows for the exploration of rich hypotheses. However, owing to the presence of interrelated sources of potential data-model misfit at multiple levels, the…
Descriptors: Measurement, Models, Bayesian Statistics, Hierarchical Linear Modeling
Hecht, Martin; Voelkle, Manuel C. – International Journal of Behavioral Development, 2021
The analysis of cross-lagged relationships is a popular approach in prevention research to explore the dynamics between constructs over time. However, a limitation of commonly used cross-lagged models is the requirement of equally spaced measurement occasions that prevents the usage of flexible longitudinal designs and complicates cross-study…
Descriptors: Models, Longitudinal Studies, Prevention, Time
Du, Han; Enders, Craig; Keller, Brian; Bradbury, Thomas N.; Karney, Benjamin R. – Grantee Submission, 2022
Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as…
Descriptors: Bayesian Statistics, Data Analysis, Computer Simulation, Sample Size