A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses

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dc.creatorTan, Zhengqi
dc.creatorde la Torre, Jimmy
dc.creatorMa, Wenchao
dc.creatorHuh, David
dc.creatorLarimer, Mary E.
dc.creatorMun, Eun-Young
dc.creator.orcid0000-0002-1820-615X (Mun, Eun-Young)
dc.creator.orcid0000-0002-2297-2770 (Tan, Zhengqi)
dc.date.accessioned2023-05-03T20:13:29Z
dc.date.available2023-05-03T20:13:29Z
dc.date.issued2022-02-04
dc.description.abstractIn research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.
dc.description.sponsorshipThe project described was supported by grants R01 AA019511 and K02 AA028630 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA).
dc.identifier.citationTan, Z., de la Torre, J., Ma, W., Huh, D., Larimer, M. E., & Mun, E. Y. (2023). A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses. Prevention science : the official journal of the Society for Prevention Research, 24(3), 480-492. https://doi.org/10.1007/s11121-022-01346-8
dc.identifier.issn1573-6695
dc.identifier.issue3
dc.identifier.urihttps://hdl.handle.net/20.500.12503/32355
dc.identifier.volume24
dc.publisherSpringer Nature
dc.relation.urihttps://doi.org/10.1007/s11121-022-01346-8
dc.rights.holder© The Author(s) 2022
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourcePrevention Science
dc.subjectassessment
dc.subjectCDM
dc.subjectclassification
dc.subjectco-occurring symptom profiles
dc.subjectDCM
dc.subjectdiagnostic classification model
dc.subjectIRT
dc.subjectresearch domain criteria
dc.subject.meshMale
dc.subject.meshYoung Adult
dc.subject.meshHumans
dc.subject.meshFemale
dc.subject.meshMental Health
dc.subject.meshMental Disorders / diagnosis
dc.subject.meshAnxiety
dc.subject.meshCognition
dc.titleA Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses
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dc.type.materialArticle

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