Cognitive diagnostic models (CDMs) or diagnostic classification models have been widely researched in educational, psychological, and psychiatric measurement, as well as many other disciplines. Over the past 30 years, a wide range of CDMs have been have been developed with the aim of providing finer-grained and multidimensional diagnostic feedback information about examinees’ strengths and weaknesses on a set of attributes (e.g., cognitive processes, skills, abilities or knowledge representations) for developing targeted feedbacks and instructional interventions.
Recent advancements in the field of CDMs have provided several useful tools and new insights about collecting, analyzing, and reporting diagnostic data. Further, recent advancements have focused on the key methodological issues in the application of the CDMs, which have included:
• The development of new item response models or structural models for cognitive diagnosis;
• The correct specification and the validation of the Q-matrix;
• The evaluation of model-data fit at the item or test level;
• The identifiability issue of the CDMs;
• The model parameter estimation methods of the CDMs and the variance–covariance matrix of the model parameters;
• Item-level model comparison;
• Differential item functioning detection, and
• The latent attribute profile estimation methods.
Therefore, the purpose of the present Research Topic is to document a collection of the latest developments in the methods and applications that contribute sustainable solutions to practical problems and issues in CDMs. We welcome the following article types: Original Research, Methods, Data Report, Brief Research Report, General Commentary, Technology and Code.
Specifically, we invite manuscripts addressing the key aims of this Topic, which include (but are not limited to):
• To introduce new methods or techniques that can be used to support practical applications to cognitive diagnostic testing;
• To evaluate or compare the behavior of the newly developed statistics or methods (such as the Q-matrix validation, model comparison, model parameter variance–covariance matrix estimation, differential item functioning detection), as well as demonstrate the merit and shortcoming of these methods;
• To provide examples of use in CDM practical applications;
• To introduce already-known methods for its new applications in real-world cognitive diagnostic assessments.
Cognitive diagnostic models (CDMs) or diagnostic classification models have been widely researched in educational, psychological, and psychiatric measurement, as well as many other disciplines. Over the past 30 years, a wide range of CDMs have been have been developed with the aim of providing finer-grained and multidimensional diagnostic feedback information about examinees’ strengths and weaknesses on a set of attributes (e.g., cognitive processes, skills, abilities or knowledge representations) for developing targeted feedbacks and instructional interventions.
Recent advancements in the field of CDMs have provided several useful tools and new insights about collecting, analyzing, and reporting diagnostic data. Further, recent advancements have focused on the key methodological issues in the application of the CDMs, which have included:
• The development of new item response models or structural models for cognitive diagnosis;
• The correct specification and the validation of the Q-matrix;
• The evaluation of model-data fit at the item or test level;
• The identifiability issue of the CDMs;
• The model parameter estimation methods of the CDMs and the variance–covariance matrix of the model parameters;
• Item-level model comparison;
• Differential item functioning detection, and
• The latent attribute profile estimation methods.
Therefore, the purpose of the present Research Topic is to document a collection of the latest developments in the methods and applications that contribute sustainable solutions to practical problems and issues in CDMs. We welcome the following article types: Original Research, Methods, Data Report, Brief Research Report, General Commentary, Technology and Code.
Specifically, we invite manuscripts addressing the key aims of this Topic, which include (but are not limited to):
• To introduce new methods or techniques that can be used to support practical applications to cognitive diagnostic testing;
• To evaluate or compare the behavior of the newly developed statistics or methods (such as the Q-matrix validation, model comparison, model parameter variance–covariance matrix estimation, differential item functioning detection), as well as demonstrate the merit and shortcoming of these methods;
• To provide examples of use in CDM practical applications;
• To introduce already-known methods for its new applications in real-world cognitive diagnostic assessments.
