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A Classification System for Instruments Frequently Found in Textbooks Used in Vocational Rehabilitation Counseling Education

dc.access.optionRestricted Campus Access Only
dc.contributor.advisorLeierer, Stephen
dc.contributor.authorSchuster, Ralf
dc.contributor.committeeMemberRowe, Dawn A
dc.contributor.committeeMemberO'Brien, Kevin F
dc.contributor.committeeMemberSligar, Steve R
dc.contributor.departmentAddictions and Rehabilitation Studies
dc.date.accessioned2021-06-18T13:41:39Z
dc.date.available2023-08-01T08:01:55Z
dc.date.created2021-08
dc.date.issued2021-04-15
dc.date.submittedAugust 2021
dc.date.updated2021-06-02T15:57:38Z
dc.degree.departmentAddictions and Rehabilitation Studies
dc.degree.disciplinePHD-Rehab Counsel Admin
dc.degree.grantorEast Carolina University
dc.degree.levelDoctoral
dc.degree.namePh.D.
dc.description.abstractThe dissertation describes the rationale and development of a classification system for instruments frequently found in textbooks used in vocational rehabilitation counseling education. Such a classification system does not yet exist but could provide benefits for vocational rehabilitation counseling educators. A system could enable consistent nomenclature for instruments and measurement domains, influence the use of assessment in vocational rehabilitation, enhance assessment practices, and stimulate research on instruments and clinical practice that uses instruments or draws from their results. A classification could also impact decisions on which instruments are selected and how they are used in education, thereby leading to potential changes in service outcomes. Sligar and Schuster (2020) coded instruments and their variables from seven graduate textbooks used in vocational rehabilitation counseling education. The limitations encountered and the data produced in this study formed the two main foundations for the present study. A qualitative meta-synthesis was conducted to synthesize and interpret this data. The k-means cluster analysis in Desktop Tableau yielded a non-hierarchical numerical cluster solution. In other words, a non-hierarchical cluster model fitted the data; a hierarchical model did not fit. The taxonomy that was constructed organized 84 instruments and 73 characteristics, and named 63 homogeneous instrument groups each representing one measurement domain. The taxonomy provides a resource for educators for identifying the instruments and domains used in rehabilitation counseling education, and as such, guiding them as to what instruments could be included in their teaching of courses. Future research could validate the instrument groups in the taxonomy and analyze all instruments covered in textbooks used in vocational rehabilitation counseling education.
dc.embargo.lift2023-08-01
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10342/9152
dc.language.isoen
dc.publisherEast Carolina University
dc.subjectclassification
dc.subjectinstruments, rehabilitation
dc.subject.meshRehabilitation, Vocational
dc.subject.meshVocational Education
dc.subject.meshCounseling
dc.titleA Classification System for Instruments Frequently Found in Textbooks Used in Vocational Rehabilitation Counseling Education
dc.typeDoctoral Dissertation
dc.type.materialtext

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