Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 3 |
Descriptor
Markov Processes | 3 |
Models | 3 |
Item Response Theory | 2 |
Monte Carlo Methods | 2 |
Test Items | 2 |
Accuracy | 1 |
Bayesian Statistics | 1 |
Classification | 1 |
Computer Assisted Instruction | 1 |
Computer Software | 1 |
Correlation | 1 |
More ▼ |
Source
Measurement:… | 3 |
Author
Culpepper, Steven | 1 |
Douglas, Jeff | 1 |
Leventhal, Brian C. | 1 |
Liang, Xinya | 1 |
Luo, Yong | 1 |
Stone, Clement A. | 1 |
Wang, Shiyu | 1 |
Zhang, Susu | 1 |
Publication Type
Journal Articles | 3 |
Reports - Research | 3 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Luo, Yong; Liang, Xinya – Measurement: Interdisciplinary Research and Perspectives, 2019
Current methods that simultaneously model differential testlet functioning (DTLF) and differential item functioning (DIF) constrain the variances of latent ability and testlet effects to be equal between the focal and the reference groups. Such a constraint can be stringent and unrealistic with real data. In this study, we propose a multigroup…
Descriptors: Test Items, Item Response Theory, Test Bias, Models
Leventhal, Brian C.; Stone, Clement A. – Measurement: Interdisciplinary Research and Perspectives, 2018
Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure,…
Descriptors: Bayesian Statistics, Item Response Theory, Models, Psychometrics
Wang, Shiyu; Zhang, Susu; Douglas, Jeff; Culpepper, Steven – Measurement: Interdisciplinary Research and Perspectives, 2018
Analyzing students' growth remains an important topic in educational research. Most recently, Diagnostic Classification Models (DCMs) have been used to track skill acquisition in a longitudinal fashion, with the purpose to provide an estimate of students' learning trajectories in terms of the change of fine-grained skills overtime. Response time…
Descriptors: Reaction Time, Markov Processes, Computer Assisted Instruction, Spatial Ability