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Sample Size Requirements for Applying Diagnostic Classification Models [PDF]
Frontiers in Psychology, 2021 Results of a comprehensive simulation study are reported investigating the effects of sample size, test length, number of attributes and base rate of mastery on item parameter recovery and classification accuracy of four DCMs (i.e., C-RUM, DINA, DINO, and LCDMREDUCED).
Sedat Sen, Allan S. Cohen
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A diagnostic classification version of Schizotypal Personality Questionnaire using diagnostic classification models [PDF]
International Journal of Methods in Psychiatric Research, 2019 AbstractObjectiveTo obtain more precise and rich information from the measurements for schizotypal personality disorder (SPD), a cutting‐edge psychometric theory called diagnostic classification models (DCMs) was first employed in the present study to develop a diagnostic classification version of the Schizotypal Personality Questionnaire (DC‐SPQ ...
Chongqin Xi +4 more
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A Semi-supervised Learning-Based Diagnostic Classification Method Using Artificial Neural Networks
Frontiers in Psychology, 2021 The purpose of cognitive diagnostic modeling (CDM) is to classify students' latent attribute profiles using their responses to the diagnostic assessment.
Kang Xue, Laine P. Bradshaw
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Modeling Learner Heterogeneity: A Mixture Learning Model With Responses and Response Times
Frontiers in Psychology, 2018 The increased popularity of computer-based testing has enabled researchers to collect various types of process data, including test takers' reaction time to assessment items, also known as response times. In recent studies, the relationship between speed
Susu Zhang, Shiyu Wang
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Language Testing in Asia, 2021 The current study compared the model fit indices, skill mastery probabilities, and classification accuracy of six Diagnostic Classification Models (DCMs): a general model (G-DINA) against five specific models (LLM, RRUM, ACDM, DINA, and DINO).
Mahdieh Shafipoor +2 more
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On the Boundary Problems in Diagnostic Classification Models
Behaviormetrika, 2021 In diagnostic classification models, parameter estimation sometimes provides estimates that stick to the boundaries of the parameter space, which is called the boundary problem and may lead to extreme values of standard errors. However, the relationship between the boundary problem and irregular standard errors has not been analytically explored.
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Poisson Diagnostic Classification Models: A Framework and an Exploratory Example
Educational and Psychological Measurement, 2021 Assessments with a large amount of small, similar, or often repetitive tasks are being used in educational, neurocognitive, and psychological contexts. For example, respondents are asked to recognize numbers or letters from a large pool of those and the number of correct answers is a count variable.
Ren Liu +3 more
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FUZZY DIAGNOSTİCS OF SOİLS ACCORDİNG TO THE WORLD REFERENCE BASE FOR SOİL RESOURCES
Сучасні інформаційні системи, 2020 Soil classification remains one of the most controversial topics in the world soil science because of differences in the principles underlying it. As of today, many countries have developed and use their own national classifications.
Samira Afrasiyab Hasanova +2 more
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JOR Spine, 2023 Background The severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time‐consuming and requires repetitive work. This study aims to develop and
Weicong Zhang +8 more
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Applied Sciences, 2023 The scoliosis report is a diagnosis made by the clinician looking at X-ray images of the spine. However, with numerous images, writing the report can be time-consuming and error-prone.
Yu Tang +4 more
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