AI Article Synopsis
- Cognitive Diagnosis Models (CDMs) are important tools used to gain detailed insights into respondents' latent traits, especially with the rising popularity of questions with multiple response options.
- Identifiability is crucial for CDMs to ensure accurate parameter estimation and valid conclusions, but most existing studies focus only on binary response models.
- This paper fills a gap by outlining the necessary and sufficient conditions for the identifiability of the DINA model when applied to polytomous data, aiding in the understanding and future research of CDMs in this context.
Article Abstract
Cognitive diagnosis models (CDMs) provide a powerful statistical and psychometric tool for researchers and practitioners to learn fine-grained diagnostic information about respondents' latent attributes. There has been a growing interest in the use of CDMs for polytomous response data, as more and more items with multiple response options become widely used. Similar to many latent variable models, the identifiability of CDMs is critical for accurate parameter estimation and valid statistical inference. However, the existing identifiability results are primarily focused on binary response models and have not adequately addressed the identifiability of CDMs with polytomous responses. This paper addresses this gap by presenting sufficient and necessary conditions for the identifiability of the widely used DINA model with polytomous responses, with the aim to provide a comprehensive understanding of the identifiability of CDMs with polytomous responses and to inform future research in this field.
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