Journal article Open Access

Sex Determination in Forensic Dentistry Using Supervised Classification Techniques

Álvarez-Vaz, Ram\ń; Sassi, Carlos


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  <identifier identifierType="DOI">10.5281/zenodo.3677593</identifier>
  <creators>
    <creator>
      <creatorName>Álvarez-Vaz, Ram\ń</creatorName>
      <givenName>Ram\ń</givenName>
      <familyName>Álvarez-Vaz</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2505-4238</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Sassi, Carlos</creatorName>
      <givenName>Carlos</givenName>
      <familyName>Sassi</familyName>
    </creator>
  </creators>
  <titles>
    <title>Sex Determination in Forensic Dentistry Using Supervised Classification Techniques</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3677593</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3677592</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">This article presents the main results obtained by characterizing the
construction of students' satisfaction at Facultad de Ciencias
Económicas y de Administración, Universidad de la República,
Uruguay, through the use and comparison of two multivariate data
analysis techniques: latent classes analysis and cluster analysis. The
used data arise survey applied to a sample of undergraduate students
of the Faculty, in the year 2009. This survey, present a structure in
blocks: on the one hand, the variables that allow making a
sociodemographic characterization of students. On the other hand
(second block) there is the ECSI model (European Customer
Satisfaction Index), which will be used to students' satisfaction
characterization. The ECSI's variables are grouped in: expectations of
the incoming students, the image that students have about the college,
teaching and services quality, the needs and personal desires about
college, and the perceived value. The main results presented in this
work consider, on the one hand, that there is indeed a variable that
refers to students' satisfaction and that it is defined by four latent
classes, from the interaction of the 6 manifest variables. On the other
hand, from the analysis of clustering through the Ward method, it is
proposed to group the students into three clusters. Finally, the results'
comparison obtained with both techniques it is also presented.</description>
  </descriptions>
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