NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 6 results Save | Export
Haiyan Liu; Wen Qu; Zhiyong Zhang; Hao Wu – Grantee Submission, 2022
Bayesian inference for structural equation models (SEMs) is increasingly popular in social and psychological sciences owing to its flexibility to adapt to more complex models and the ability to include prior information if available. However, there are two major hurdles in using the traditional Bayesian SEM in practice: (1) the information nested…
Descriptors: Bayesian Statistics, Structural Equation Models, Statistical Inference, Statistical Distributions
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
Peer reviewed Peer reviewed
Direct linkDirect link
Vegetabile, Brian G.; Stout-Oswald, Stephanie A.; Davis, Elysia Poggi; Baram, Tallie Z.; Stern, Hal S. – Journal of Educational and Behavioral Statistics, 2019
Predictability of behavior is an important characteristic in many fields including biology, medicine, marketing, and education. When a sequence of actions performed by an individual can be modeled as a stationary time-homogeneous Markov chain the predictability of the individual's behavior can be quantified by the entropy rate of the process. This…
Descriptors: Markov Processes, Prediction, Behavior, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
Johnson, Timothy R. – Applied Psychological Measurement, 2013
One of the distinctions between classical test theory and item response theory is that the former focuses on sum scores and their relationship to true scores, whereas the latter concerns item responses and their relationship to latent scores. Although item response theory is often viewed as the richer of the two theories, sum scores are still…
Descriptors: Item Response Theory, Scores, Computation, Bayesian Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Lu, Zhenqiu Laura; Zhang, Zhiyong; Lubke, Gitta – Multivariate Behavioral Research, 2011
"Growth mixture models" (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class…
Descriptors: Bayesian Statistics, Statistical Inference, Computation, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Fox, J.-P.; Wyrick, Cheryl – Journal of Educational and Behavioral Statistics, 2008
The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear…
Descriptors: Item Response Theory, Models, Markov Processes, Monte Carlo Methods