A Content Originality Analysis of HRD Focused Dissertations and Published Academic Articles Using Turnitin Plagiarism Detection Software

Autor/in	Mayes, Robin James
Titel	A Content Originality Analysis of HRD Focused Dissertations and Published Academic Articles Using Turnitin Plagiarism Detection Software
Quelle	(2017), (177 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, University of North Texas
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-0-3555-6008-4
Schlagwörter	Hochschulschrift; Dissertation; Plagiarism; Computer Software; Doctoral Dissertations; Regression (Statistics); Content Analysis; Computational Linguistics; Human Resources; Labor Force Development; Career Development; Organizational Development; Prediction; Publications; Citations (References); Graduate Students; Comparative Analysis + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Plagiat; Doctoral dissertation; Doctoral thesis; Doctoral theses; Dissertationsschrift; Regression; Regressionsanalyse; Inhaltsanalyse; Linguistics; Computerlinguistik; Humankapital; Arbeitskräftebestand; Berufsentwicklung; Organisationsentwicklung; Vorhersage; Citations; Zitat; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin
Abstract	This empirical exploratory study quantitatively analyzed content similarity indices (potential plagiarism) from a corpus consisting of 360 dissertations and 360 published articles. The population was defined using the filtering search criteria human resource development, training and development, organizational development, career development, or HRD. This study described in detail the process of collecting content similarity analysis (CSA) metadata using Turnitin software (www.turnitin.com). This researcher conducted robust descriptive statistics, a Wilcoxon signed-rank statistic between the similarity indices before and after false positives were excluded, and a multinomial logistic regression analysis to predict levels of plagiarism for the dissertations and the published articles. The corpus of dissertations had an adjusted rate of document similarity (potential plagiarism) of "M" = 9%, ("SD" = 6%) with 88.1% of the dissertations in the low level of plagiarism, 9.7% in the high and 2.2% in the excessive group. The corpus of published articles had an adjusted rate of document similarity (potential plagiarism) of "M" = 11%, ("SD" = 10%) with 79.2% of the published articles in the low level of plagiarism, 12.8% in the high and 8.1% in the excessive group. Most of the difference between the dissertations and published articles were attributed to plagiarism-of-self issues which were absent in the dissertations. Statistics were also conducted which returned a statistically significant justification for employing the investigative process of removing false positives, thereby adjusting the Turnitin results. This study also found two independent variables (reference and word counts) that predicted dissertation membership in the high (0.15-0.24) and excessive level (0.25-1.00) of plagiarism and published article membership in the excessive level (0.25-1.00) of plagiarism. I used multinomial logistic regression to establish the optimal prediction model. The multinomial logistic regression results for the dissertations returned a Nagelkerke pseudo R[superscript 2] of 0.169 and for the published articles a Nagelkerke pseudo R[superscript 2] 0.095. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01