Academic quality measurement: A multivariate approach
- Autores
- Redchuk, Andrés; Moguerza, Javier M.; Cardone Riportella, Clara Laura
- Año de publicación
- 2011
- Idioma
- inglés
- Tipo de recurso
- documento de trabajo
- Estado
- versión publicada
- Descripción
- This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. Themethodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data setswhere we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combinedwith Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a setof dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduateprogramme. Among the most important conclusion we say the methodology presented in this work has the following advantages:Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student,showing the key factors for improving quality. Student weights can be related to student characteristics to make marketsegmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can bedetermined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process orSWOT analysis).
Premio al mejor “Working Paper” del Departamento de Economía Financiera y Contabilidad de la Universidad Pablo de Olavide (edición 2011). - Materia
-
Economía y Negocios
Quality Measurement
Posgraduated Progrmme
Nonparametric Model - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Repositorio
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/5081
Ver los metadatos del registro completo
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Academic quality measurement: A multivariate approachRedchuk, AndrésMoguerza, Javier M.Cardone Riportella, Clara LauraEconomía y NegociosQuality MeasurementPosgraduated ProgrmmeNonparametric ModelThis paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. Themethodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data setswhere we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combinedwith Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a setof dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduateprogramme. Among the most important conclusion we say the methodology presented in this work has the following advantages:Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student,showing the key factors for improving quality. Student weights can be related to student characteristics to make marketsegmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can bedetermined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process orSWOT analysis).Premio al mejor “Working Paper” del Departamento de Economía Financiera y Contabilidad de la Universidad Pablo de Olavide (edición 2011).2011-12info:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_8042info:ar-repo/semantics/documentoDeTrabajoapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/5081enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:19Zoai:digital.cic.gba.gob.ar:11746/5081Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:20.057CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse |
dc.title.none.fl_str_mv |
Academic quality measurement: A multivariate approach |
title |
Academic quality measurement: A multivariate approach |
spellingShingle |
Academic quality measurement: A multivariate approach Redchuk, Andrés Economía y Negocios Quality Measurement Posgraduated Progrmme Nonparametric Model |
title_short |
Academic quality measurement: A multivariate approach |
title_full |
Academic quality measurement: A multivariate approach |
title_fullStr |
Academic quality measurement: A multivariate approach |
title_full_unstemmed |
Academic quality measurement: A multivariate approach |
title_sort |
Academic quality measurement: A multivariate approach |
dc.creator.none.fl_str_mv |
Redchuk, Andrés Moguerza, Javier M. Cardone Riportella, Clara Laura |
author |
Redchuk, Andrés |
author_facet |
Redchuk, Andrés Moguerza, Javier M. Cardone Riportella, Clara Laura |
author_role |
author |
author2 |
Moguerza, Javier M. Cardone Riportella, Clara Laura |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Economía y Negocios Quality Measurement Posgraduated Progrmme Nonparametric Model |
topic |
Economía y Negocios Quality Measurement Posgraduated Progrmme Nonparametric Model |
dc.description.none.fl_txt_mv |
This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. Themethodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data setswhere we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combinedwith Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a setof dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduateprogramme. Among the most important conclusion we say the methodology presented in this work has the following advantages:Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student,showing the key factors for improving quality. Student weights can be related to student characteristics to make marketsegmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can bedetermined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process orSWOT analysis). Premio al mejor “Working Paper” del Departamento de Economía Financiera y Contabilidad de la Universidad Pablo de Olavide (edición 2011). |
description |
This paper applies a new quality measurement methodology to measure the quality of the postgraduate courses. Themethodology we propose is the Academic Quality Measurement (AQM). The model is applied to several simulated data setswhere we know the true value of the parameters of the model. A nonparametric model, based in Nearest Neighbours combinedwith Restricted Least Squared methods, is developed in which students evaluate the overall academic programme quality and a setof dimensions or attributes that determine this quality. The database comes from a Spanish Public University post graduateprogramme. Among the most important conclusion we say the methodology presented in this work has the following advantages:Knowledge of the attribute weights allow the ordering of the attributes according to their relative importance to the student,showing the key factors for improving quality. Student weights can be related to student characteristics to make marketsegmentation directly linked to quality objectives. The relative strengths and weaknesses of the service (high educations) can bedetermined by comparing the mean value of the attributes of the service to the values of other companies (Benchmark process orSWOT analysis). |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-12 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/workingPaper info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_8042 info:ar-repo/semantics/documentoDeTrabajo |
format |
workingPaper |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://digital.cic.gba.gob.ar/handle/11746/5081 |
url |
https://digital.cic.gba.gob.ar/handle/11746/5081 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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application/pdf |
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reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
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CIC Digital (CICBA) |
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CIC Digital (CICBA) |
instname_str |
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
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CICBA |
institution |
CICBA |
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CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
repository.mail.fl_str_mv |
marisa.degiusti@sedici.unlp.edu.ar |
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13.070432 |