Ningzhong Shi
Biographic Data
| ID | 7980867 |
|---|---|
| NAME | Ningzhong Shi |
| GIVEN NAMES | Ningzhong |
| FAMILY NAME | Shi |
| SIGNATURE | SHI N |
| AFFILIATIONS | Northeast Normal University |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
A Mixture Response Time Process Model for Aberrant Behaviors and Item Nonresponses
In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008 van der Linden, W. J., & Guo, F. (2008). Bayesian procedures for identifying aberrant response–time patterns in adaptive testing. Psychometrika, 73(3), 365–384. https://doi.org/10.1007/s11336-007-9046-8[Crossref], [Web of Science ®] , [Google Scholar]; Wang & Xu, 20…
Sequential Gibbs Sampling Algorithm for Cognitive Diagnosis Models with Many Attributes
Cognitive diagnosis models (CDMs) are useful statistical tools to provide rich information relevant for intervention and learning. As a popular approach to estimate and make inference of CDMs, the Markov chain Monte Carlo (MCMC) algorithm is widely used in practice. However, when the number of attributes, K, is large, the existing MCMC algorithm may become time-consuming, due to the fact that O(2K) calculations are usually needed in the process o…
Latent variable models for analyzing children's processing ability
In this paper, we develop a latent processing ability model to analyze the speed of processing ability data. Our approach can not only effectively evaluate the effects of covariates on the latent processing ability, but also estimate the latent trait of each child by calculating its posterior mean. In addition, we derive the correlations structure of latent traits among different age groups. Simulations are conducted to evaluate the performance o…
Refining the two‐parameter testlet response model by introducing testlet discrimination parameters
For testlet response data, traditional item response theory ( IRT ) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet‐based IRT models have been developed to model examinees' responses. In this paper, a new two‐parameter normal ogive testlet response theory ( 2PNOTRT ) model for dichotomous items is proposed by introducing testlet discrimination parameters. A Bayesian model par…
Analyzing Longitudinal Item Response Data via the Pairwise Fitting Method
Multidimensional item response theory (MIRT) models can be applied to longitudinal educational surveys where a group of individuals are administered different tests over time with some common items. However, computational problems typically arise as the dimension of the latent variables increases. This is especially true when the latent variable distribution cannot be integrated out analytically, as with MIRT models for binary data. In this artic…
Process analysis and level measurement of textbooks use by teachers
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Process analysis and level measurement of textbooks use by teachers
Analyzing Longitudinal Item Response Data via the Pairwise Fitting Method
Multidimensional item response theory (MIRT) models can be applied to longitudinal educational surveys where a group of individuals are administered different tests over time with some common items. However, computational problems typically arise as the dimension of the latent variables increases. This is especially true when the latent variable distribution cannot be integrated out analytically, as with MIRT models for binary data. In this artic…
Refining the two‐parameter testlet response model by introducing testlet discrimination parameters
For testlet response data, traditional item response theory ( IRT ) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet‐based IRT models have been developed to model examinees' responses. In this paper, a new two‐parameter normal ogive testlet response theory ( 2PNOTRT ) model for dichotomous items is proposed by introducing testlet discrimination parameters. A Bayesian model par…
Latent variable models for analyzing children's processing ability
In this paper, we develop a latent processing ability model to analyze the speed of processing ability data. Our approach can not only effectively evaluate the effects of covariates on the latent processing ability, but also estimate the latent trait of each child by calculating its posterior mean. In addition, we derive the correlations structure of latent traits among different age groups. Simulations are conducted to evaluate the performance o…
Sequential Gibbs Sampling Algorithm for Cognitive Diagnosis Models with Many Attributes
Cognitive diagnosis models (CDMs) are useful statistical tools to provide rich information relevant for intervention and learning. As a popular approach to estimate and make inference of CDMs, the Markov chain Monte Carlo (MCMC) algorithm is widely used in practice. However, when the number of attributes, K, is large, the existing MCMC algorithm may become time-consuming, due to the fact that O(2K) calculations are usually needed in the process o…
A Mixture Response Time Process Model for Aberrant Behaviors and Item Nonresponses
In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008 van der Linden, W. J., & Guo, F. (2008). Bayesian procedures for identifying aberrant response–time patterns in adaptive testing. Psychometrika, 73(3), 365–384. https://doi.org/10.1007/s11336-007-9046-8[Crossref], [Web of Science ®] , [Google Scholar]; Wang & Xu, 20…
Computer Science (6 works) · Mathematics (5 works) · Statistics (5 works) · Artificial Intelligence (3 works) · Artificial Intelligence (3 works) · Bayesian probability (3 works) · Item response theory (3 works) · Psychometric Methodologies and Testing (3 works) · Psychometrics (3 works) · Advanced Statistical Modeling Techniques (2 works)