Original Article
A sequential cognitive diagnosis model for polytomous responses
Abstract
This paper proposes a general polytomous cognitive diagnosis model for a special type of graded responses, where item categories are attained in a sequential manner, and associated with some attributes explicitly. To relate categories to attributes, a category‐level Q‐matrix is used. When the attribute and category association is specified a priori, the proposed model has the flexibility to allow different cognitive processes (e.g., conjunctive, disjunctive) to be modelled at different categories within a single item. This model can be extended for items where categories cannot be explicitly linked to attributes, and for items with unordered categories. The feasibility of the proposed model is examined using simulated data. The proposed model is illustrated using the data from the Trends in International Mathematics and Science Study 2007 assessment.
Citing Literature
Number of times cited according to CrossRef: 35
- Xi-tian Chen, Zhengjia Dai, Ying Lin, undefined, 2020 International Joint Conference on Neural Networks (IJCNN), 10.1109/IJCNN48605.2020.9207587, (1-8), (2020).
- Shiyu Wang, Yiling Hu, Qi Wang, Bian Wu, Yawei Shen, Martha Carr, The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills, Frontiers in Psychology, 10.3389/fpsyg.2020.00305, 11, (2020).
- Xiaomin Li, Wen-Chung Wang, Qin Xie, Cognitive Diagnostic Models for Rater Effects, Frontiers in Psychology, 10.3389/fpsyg.2020.00525, 11, (2020).
- Lei Guo, Jing Yang, Naiqing Song, Spectral Clustering Algorithm for Cognitive Diagnostic Assessment, Frontiers in Psychology, 10.3389/fpsyg.2020.00944, 11, (2020).
- Xuliang Gao, Wenchao Ma, Daxun Wang, Yan Cai, Dongbo Tu, A Class of Cognitive Diagnosis Models for Polytomous Data, Journal of Educational and Behavioral Statistics, 10.3102/1076998620951986, (107699862095198), (2020).
- Luca Stefanutti, Debora de Chiusole, Pasquale Anselmi, Andrea Spoto, Extending the Basic Local Independence Model to Polytomous Data, Psychometrika, 10.1007/s11336-020-09722-5, (2020).
- Xue Zhang, Juntao Wang, On the Sequential Hierarchical Cognitive Diagnostic Model, Frontiers in Psychology, 10.3389/fpsyg.2020.579018, 11, (2020).
- Wenchao Ma, Jimmy Torre, An empirical Q‐matrix validation method for the sequential generalized DINA model, British Journal of Mathematical and Statistical Psychology, 10.1111/bmsp.12156, 73, 1, (142-163), (2019).
- Hong Jiao, Dandan Liao, Peida Zhan, Utilizing Process Data for Cognitive Diagnosis, Handbook of Diagnostic Classification Models, 10.1007/978-3-030-05584-4_20, (421-436), (2019).
- Alexander Robitzsch, Ann Cathrice George, The R Package CDM for Diagnostic Modeling, Handbook of Diagnostic Classification Models, 10.1007/978-3-030-05584-4_26, (549-572), (2019).
- Jimmy de la Torre, Nathan D. Minchen, The G-DINA Model Framework, Handbook of Diagnostic Classification Models, 10.1007/978-3-030-05584-4_7, (155-169), (2019).
- Wenchao Ma, Cognitive Diagnosis Modeling Using the GDINA R Package, Handbook of Diagnostic Classification Models, 10.1007/978-3-030-05584-4_29, (593-601), (2019).
- Wenchao Ma, Wenjing Guo, Cognitive diagnosis models for multiple strategies, British Journal of Mathematical and Statistical Psychology, 10.1111/bmsp.12155, 72, 2, (370-392), (2019).
- Zhehan Jiang, Kevin Walker, Dexin Shi, Applying AdaBoost to Improve Diagnostic Accuracy, Methodology, 10.1027/1614-2241/a000166, 15, 2, (77-87), (2019).
- Pablo Nájera, Miguel A. Sorrel, Francisco José Abad, Reconsidering Cutoff Points in the General Method of Empirical Q-Matrix Validation, Educational and Psychological Measurement, 10.1177/0013164418822700, (001316441882270), (2019).
- Peida Zhan, Hong Jiao, Kaiwen Man, Lijun Wang, Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial, Journal of Educational and Behavioral Statistics, 10.3102/1076998619826040, (107699861982604), (2019).
- Peida Zhan, Wenchao Ma, Hong Jiao, Shuliang Ding, A Sequential Higher Order Latent Structural Model for Hierarchical Attributes in Cognitive Diagnostic Assessments, Applied Psychological Measurement, 10.1177/0146621619832935, (014662161983293), (2019).
- Xuliang Gao, Daxun Wang, Yan Cai, Dongbo Tu, Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items, Journal of Classification, 10.1007/s00357-019-09357-x, (2019).
- Zhehan Jiang, Using the iterative latent-class analysis approach to improve attribute accuracy in diagnostic classification models, Behavior Research Methods, 10.3758/s13428-018-01191-0, (2019).
- Wenchao Ma, Evaluating the Fit of Sequential G-DINA Model Using Limited-Information Measures, Applied Psychological Measurement, 10.1177/0146621619843829, (014662161984382), (2019).
- Ren Liu, Zhehan Jiang, A general diagnostic classification model for rating scales, Behavior Research Methods, 10.3758/s13428-019-01239-9, (2019).
- Steven Andrew Culpepper, An Exploratory Diagnostic Model for Ordinal Responses with Binary Attributes: Identifiability and Estimation, Psychometrika, 10.1007/s11336-019-09683-4, (2019).
- Peida Zhan, Hong Jiao, Manqian Liao, Yufang Bian, Bayesian DINA Modeling Incorporating Within-Item Characteristic Dependency, Applied Psychological Measurement, 10.1177/0146621618781594, 43, 2, (143-158), (2018).
- Dongbo Tu, Shiyu Wang, Yan Cai, Jeff Douglas, Hua-Hua Chang, Cognitive Diagnostic Models With Attribute Hierarchies: Model Estimation With a Restricted Q-Matrix Design, Applied Psychological Measurement, 10.1177/0146621618765721, 43, 4, (255-271), (2018).
- Wenchao Ma, A diagnostic tree model for polytomous responses with multiple strategies, British Journal of Mathematical and Statistical Psychology, 10.1111/bmsp.12137, 72, 1, (61-82), (2018).
- Yunxiao Chen, Yang Liu, Shuangshuang Xu, Mutual Information Reliability for Latent Class Analysis, Applied Psychological Measurement, 10.1177/0146621617748324, 42, 6, (460-477), (2018).
- Wenchao Ma, Diagnostic Measurement: Theory, Methods, and Applications, Measurement: Interdisciplinary Research and Perspectives, 10.1080/15366367.2018.1434349, 16, 1, (78-79), (2018).
- Wenchao Ma, Jimmy de la Torre, Category-Level Model Selection for the Sequential G-DINA Model, Journal of Educational and Behavioral Statistics, 10.3102/1076998618792484, (107699861879248), (2018).
- Ren Liu, Zhehan Jiang, Diagnostic Classification Models for Ordinal Item Responses, Frontiers in Psychology, 10.3389/fpsyg.2018.02512, 9, (2018).
- Su-Pin Hung, Hung-Yu Huang, A Sequential Process Model for Cognitive Diagnostic Assessment With Repeated Attempts, Applied Psychological Measurement, 10.1177/0146621618813111, (014662161881311), (2018).
- Peida Zhan, Wen-Chung Wang, Hong Jiao, Yufang Bian, Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis, Frontiers in Psychology, 10.3389/fpsyg.2018.00997, 9, (2018).
- Jinsong Chen, Jimmy de la Torre, Introducing the General Polytomous Diagnosis Modeling Framework, Frontiers in Psychology, 10.3389/fpsyg.2018.01474, 9, (2018).
- Huey-Min Wu, Online individualised tutor for improving mathematics learning: a cognitive diagnostic model approach, Educational Psychology, 10.1080/01443410.2018.1494819, (1-15), (2018).
- Peida Zhan, Hong Jiao, Dandan Liao, Cognitive diagnosis modelling incorporating item response times, British Journal of Mathematical and Statistical Psychology, 10.1111/bmsp.12114, 71, 2, (262-286), (2017).
- Nathan D. Minchen, Jimmy de la Torre, Ying Liu, A Cognitive Diagnosis Model for Continuous Response, Journal of Educational and Behavioral Statistics, 10.3102/1076998617703060, 42, 6, (651-677), (2017).
