A general diagnostic modelling framework for forced-choice assessments

Diagnostic classification modelling (DCM) is a family of restricted latent class models often used in educational settings to assess students' strengths and weaknesses. Recently, there has been growing interest in applying DCM to noncognitive traits in fields such as clinical and organizational...

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Detalhes bibliográficos
Autores: Najera Álvarez, Pablo, Schames Kreitchmann, Rodrigo, Escudero, Scarlett, Torre, Jimmy de la, Abad García, Francisco José, Sorrel Luján, Miguel Ángel
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/719990
Acesso em linha:http://hdl.handle.net/10486/719990
https://dx.doi.org/10.1111/bmsp.12393
Access Level:acceso abierto
Palavra-chave:diagnostic classification
forced-choice assessments
latent class
noncognitive traits
Psicología
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spelling A general diagnostic modelling framework for forced-choice assessmentsNajera Álvarez, PabloSchames Kreitchmann, RodrigoEscudero, ScarlettTorre, Jimmy de laAbad García, Francisco JoséSorrel Luján, Miguel Ángeldiagnostic classificationforced-choice assessmentslatent classnoncognitive traitsPsicologíaDiagnostic classification modelling (DCM) is a family of restricted latent class models often used in educational settings to assess students' strengths and weaknesses. Recently, there has been growing interest in applying DCM to noncognitive traits in fields such as clinical and organizational psychology, as well as personality profiling. To address common response biases in these assessments, such as social desirability, Huang (2023, Educational and Psychological Measurement, 83, 146) adopted the forced-choice (FC) item format within the DCM framework, developing the FC-DCM. This model assumes that examinees with no clear preference for any statements in an FC block will choose completely at random. Additionally, the unique parametrization of the FC-DCM poses challenges for integration with established DCM frameworks in the literature. In the present study, we enhance the capabilities of DCM by introducing a general diagnostic framework for FC assessments. We present an adaptation of the G-DINA model to accommodate FC responses. Simulation results show that the G-DINA model provides accurate classifications, item parameter estimates and attribute correlations, outperforming the FC-DCM in realistic scenarios where item discrimination varies. A real FC assessment example further illustrates the better model fit of the G-DINA. Practical recommendations for using the FC format in diagnostic assessments of noncognitive traits are providedUniversidad Pontificia Comillas (2024 Call for Funding of Internal Research Projects: “Advancements in cognitive diagnosis models for formative assessments”); MICIU/AEI/10.13039/501100011033 and ERDF/EU under the project “Computerized adaptive tests based on new assessment formats” (reference: PID2022-137258NB-I00); UAM-IIC Chair Psychometric Models and ApplicationsWileyDepartamento de Psicología Social y MetodologíaFacultad de Psicología20252025-04-23research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/719990https://dx.doi.org/10.1111/bmsp.12393reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7199902026-06-23T12:46:27Z
dc.title.none.fl_str_mv A general diagnostic modelling framework for forced-choice assessments
title A general diagnostic modelling framework for forced-choice assessments
spellingShingle A general diagnostic modelling framework for forced-choice assessments
Najera Álvarez, Pablo
diagnostic classification
forced-choice assessments
latent class
noncognitive traits
Psicología
title_short A general diagnostic modelling framework for forced-choice assessments
title_full A general diagnostic modelling framework for forced-choice assessments
title_fullStr A general diagnostic modelling framework for forced-choice assessments
title_full_unstemmed A general diagnostic modelling framework for forced-choice assessments
title_sort A general diagnostic modelling framework for forced-choice assessments
dc.creator.none.fl_str_mv Najera Álvarez, Pablo
Schames Kreitchmann, Rodrigo
Escudero, Scarlett
Torre, Jimmy de la
Abad García, Francisco José
Sorrel Luján, Miguel Ángel
author Najera Álvarez, Pablo
author_facet Najera Álvarez, Pablo
Schames Kreitchmann, Rodrigo
Escudero, Scarlett
Torre, Jimmy de la
Abad García, Francisco José
Sorrel Luján, Miguel Ángel
author_role author
author2 Schames Kreitchmann, Rodrigo
Escudero, Scarlett
Torre, Jimmy de la
Abad García, Francisco José
Sorrel Luján, Miguel Ángel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Psicología Social y Metodología
Facultad de Psicología
dc.subject.none.fl_str_mv diagnostic classification
forced-choice assessments
latent class
noncognitive traits
Psicología
topic diagnostic classification
forced-choice assessments
latent class
noncognitive traits
Psicología
description Diagnostic classification modelling (DCM) is a family of restricted latent class models often used in educational settings to assess students' strengths and weaknesses. Recently, there has been growing interest in applying DCM to noncognitive traits in fields such as clinical and organizational psychology, as well as personality profiling. To address common response biases in these assessments, such as social desirability, Huang (2023, Educational and Psychological Measurement, 83, 146) adopted the forced-choice (FC) item format within the DCM framework, developing the FC-DCM. This model assumes that examinees with no clear preference for any statements in an FC block will choose completely at random. Additionally, the unique parametrization of the FC-DCM poses challenges for integration with established DCM frameworks in the literature. In the present study, we enhance the capabilities of DCM by introducing a general diagnostic framework for FC assessments. We present an adaptation of the G-DINA model to accommodate FC responses. Simulation results show that the G-DINA model provides accurate classifications, item parameter estimates and attribute correlations, outperforming the FC-DCM in realistic scenarios where item discrimination varies. A real FC assessment example further illustrates the better model fit of the G-DINA. Practical recommendations for using the FC format in diagnostic assessments of noncognitive traits are provided
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-04-23
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/719990
https://dx.doi.org/10.1111/bmsp.12393
url http://hdl.handle.net/10486/719990
https://dx.doi.org/10.1111/bmsp.12393
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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