A Content Originality Analysis of HRD Focused Dissertations and Published Academic Articles using TurnItIn Plagiarism Detection Software
Description: 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 (.15-.24) and excessive level (.25-1.00) of plagiarism and published article membership in the excessive level (.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 R2 of .169 and for the published articles a Nagelkerke pseudo R2 ...
Date: May 2017
Creator: Mayes, Robin James