Use of Clustering Techniques in Deriving Psychoeducational Profiles.
This study explores whether a synthesis of clinical and statistical data taken from the psychoeducational reports completed on a group of 42, 9- to 11-year-old boys referred to a Child Psychiatric Outpatient Department for school-learning problems, would yield discrete clinical categories or cluster...
| Publicado en: | Contemporary Educational Psychology Vol. 7; no. 1; pp. 81 - 90 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Jan1982
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=22039605&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 22039605 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0361476X CEP jtl: Contemporary Educational Psychology issn: 0361476X maglogo: N pubinfo: dt: Jan1982 vid: 7 iid: 1 pid: 735 pub: Academic Press Inc. artinfo: ui: 22039605 10.1016/0361-476X(82)90010-8 ppf: 81 ppct: 9 formats: tig: atl: Use of Clustering Techniques in Deriving Psychoeducational Profiles. aug: au: Sinclair, Esther Kheifets, Leeka affil: Neuropsychiatric Institute, University of California, Los Angeles. su: Educational psychology Academic achievement Age groups Students Tutors & tutoring Educational sociology sug: subj: Educational psychology Academic achievement Age groups Students Tutors & tutoring Educational sociology ab: This study explores whether a synthesis of clinical and statistical data taken from the psychoeducational reports completed on a group of 42, 9- to 11-year-old boys referred to a Child Psychiatric Outpatient Department for school-learning problems, would yield discrete clinical categories or clusters of children. An amalgamated hierarchical clustering technique which formed clusters by subjects based on a measure of euclidean distance was used. Forty-two reports were evaluated by licensed educational psychologists in five input areas: Developmental History, School History, Cognitive Functioning, Sensorimotor/Perceptual Functioning, and Academic Achievement. One of three educational placement recommendations was identified for each subject: No Educational Placement Intervention Necessary, Special Tutoring or Remediation, and Special Class Placement. Using clinical characteristic ratings given by the evaluators on each variable, a similarity-dissimilarity matrix was formed which classified subjects into four discrete clusters based on their clinical profiles. A χ test determined that there was a significant association (p < .01) between cluster membership and educational placement recommendations. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 1982 holdings: @attributes: islocal: N |
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