ArticleApplied psychological measurement2023
Applying Negative Binomial Distribution in Diagnostic Classification Models for Analyzing Count Data.
Article in Applied psychological measurement, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Distinct drivers of stadium attendance and online streaming: evidence from the Chinese women's super league.Frontiers in sports and active living · 2025Article
- New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data.Psychometrika · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
Diagnostic classification models (DCMs) have been used to classify examinees into groups based on their possession status of a set of latent traits. In addition to traditional item-based scoring approaches, examinees may be scored based on their completion of a series of small and similar tasks. Those scores are usually considered as count variables. To model count scores, this study proposes a new class of DCMs that uses the negative binomial distribution at its core. We explained the proposed model framework and demonstrated its use through an operational example. Simulation studies were conducted to evaluate the performance of the proposed model and compare it with the Poisson-based DCM.
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Registered trials
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