Evidence map›Paper›PMID 38517594›Full record

ArticlePsychometrika2024

Sufficient and Necessary Conditions for the Identifiability of DINA Models with Polytomous Responses.

Mengqi Lin, Gongjun Xu

Abstract read
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In one paragraph

Article in Psychometrika, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mengqi LinDepartment of Statistics, University of Michigan, 456 West Hall, 1085 South University, Ann Arbor, 48109, MI, USA.
Gongjun XuDepartment of Statistics, University of Michigan, 456 West Hall, 1085 South University, Ann Arbor, 48109, MI, USA. gongjun@umich.edu.ORCID http://orcid.org/0000-0003-4023-5413

Funding

National Science Foundation SES-1846747National Science Foundation SES-2150601
6 · The paper itself

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.

Indexed as

Models, StatisticalPsychometricsCognitionHumanscognitive diagnosis modelsDINA modelidentifiabilitypolytomous responses

Identifiers

PMID38517594

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.