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
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/5081

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oai_identifier_str oai:digital.cic.gba.gob.ar:11746/5081
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling 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
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dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/5081
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dc.language.none.fl_str_mv eng
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