Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling

Detalhes bibliográficos
Autor(a) principal: Gomes, A.
Data de Publicação: 2015
Outros Autores: Dias, J. G.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/10908
Resumo: Latent growth mixture modeling is a statistical approach that models longitudinal data, grouping individuals who share similar longitudinal data patterns into latent classes. We evaluated the application of this method in a sample of ab initio pilot applicants (N = 297), using longitudinal data collected from a military flight-screening program (where the applicants flew seven required flights), resulting in a final pass–fail outcome. Results showed the existence of a two-class solution (Cluster 1 presented an initially higher performance and contained 75% of the Pass candidates) and the psychomotor coordination and general adaptability showed a significant effect.
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spelling Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modelingLatent growth mixture modeling is a statistical approach that models longitudinal data, grouping individuals who share similar longitudinal data patterns into latent classes. We evaluated the application of this method in a sample of ab initio pilot applicants (N = 297), using longitudinal data collected from a military flight-screening program (where the applicants flew seven required flights), resulting in a final pass–fail outcome. Results showed the existence of a two-class solution (Cluster 1 presented an initially higher performance and contained 75% of the Pass candidates) and the psychomotor coordination and general adaptability showed a significant effect.Taylor and Francis2016-02-22T12:04:43Z2015-01-01T00:00:00Z20152019-05-16T10:36:55Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/10908eng1050-841410.1080/10508414.2015.1130489Gomes, A.Dias, J. G.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:44:11Zoai:repositorio.iscte-iul.pt:10071/10908Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:20:56.848471Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
title Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
spellingShingle Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
Gomes, A.
title_short Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
title_full Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
title_fullStr Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
title_full_unstemmed Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
title_sort Improving the selection of pilot air force candidates using latent trajectories: an application of latent growth mixture modeling
author Gomes, A.
author_facet Gomes, A.
Dias, J. G.
author_role author
author2 Dias, J. G.
author2_role author
dc.contributor.author.fl_str_mv Gomes, A.
Dias, J. G.
description Latent growth mixture modeling is a statistical approach that models longitudinal data, grouping individuals who share similar longitudinal data patterns into latent classes. We evaluated the application of this method in a sample of ab initio pilot applicants (N = 297), using longitudinal data collected from a military flight-screening program (where the applicants flew seven required flights), resulting in a final pass–fail outcome. Results showed the existence of a two-class solution (Cluster 1 presented an initially higher performance and contained 75% of the Pass candidates) and the psychomotor coordination and general adaptability showed a significant effect.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01T00:00:00Z
2015
2016-02-22T12:04:43Z
2019-05-16T10:36:55Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/10908
url http://hdl.handle.net/10071/10908
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1050-8414
10.1080/10508414.2015.1130489
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor and Francis
publisher.none.fl_str_mv Taylor and Francis
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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