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A Correlational Study on the Predictive Relationship Between Scholarly Impact and Research Institution Performance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Correlational Study on the Predictive Relationship Between Scholarly Impact and Research Institution Performance./
Author:	Flynn, Donald F.
Description:	1 online resource (145 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29214887click for full text (PQDT)
ISBN:	9798802769379

A Correlational Study on the Predictive Relationship Between Scholarly Impact and Research Institution Performance.
Flynn, Donald F.

A Correlational Study on the Predictive Relationship Between Scholarly Impact and Research Institution Performance. - 1 online resource (145 pages)

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (D.C.S.)--Colorado Technical University, 2022.

Includes bibliographical references

The study problem addressed the lack of a predictive analytic relationship between scholarly impact and research institution performance (Donner et al., 2020). The value in determining a relationship addresses a gap in bibliometrics, the science of quantitatively measuring the productivity and quality of research outcomes. The study leveraged a quantitative nonexperimental correlational design to determine the predictive analytic relationship between the two components. Historically, bibliometrics tends to be author-centric, and the lack of mature bibliometric indicators describing the research institution and the relationship to scholarly impact creates risk for the institution and weakens credibility with evaluation communities (Cox et al., 2019). The study depended on the research question: What is the predictive analytic relationship between scholarly impact and research institution performance? The population for the study leveraged Elsevier Scopus, a leading citation database for scientific literature with over 75 million total records from more than 80,000 institutions. A randomly selected sample set of 385 institutions was adequate to provide a margin of error of 5% at the 95% confidence level. The study used the R programming language to execute regression analysis, resulting in statistical significance for each predictor variable independently on the dependent variable. The findings provided the construction of predictive analytic models and addressed the hypotheses. Lastly, the results included a multiple regression model where analysis of the independent variables found that at F(5, 379) = 4445, p < .000 with an adjusted R2 of 0.983, the model is statistically significant.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798802769379Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Assessing scholarly outputIndex Terms--Genre/Form:

542853
Electronic books.

A Correlational Study on the Predictive Relationship Between Scholarly Impact and Research Institution Performance.
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504 $a Includes bibliographical references
520 $a The study problem addressed the lack of a predictive analytic relationship between scholarly impact and research institution performance (Donner et al., 2020). The value in determining a relationship addresses a gap in bibliometrics, the science of quantitatively measuring the productivity and quality of research outcomes. The study leveraged a quantitative nonexperimental correlational design to determine the predictive analytic relationship between the two components. Historically, bibliometrics tends to be author-centric, and the lack of mature bibliometric indicators describing the research institution and the relationship to scholarly impact creates risk for the institution and weakens credibility with evaluation communities (Cox et al., 2019). The study depended on the research question: What is the predictive analytic relationship between scholarly impact and research institution performance? The population for the study leveraged Elsevier Scopus, a leading citation database for scientific literature with over 75 million total records from more than 80,000 institutions. A randomly selected sample set of 385 institutions was adequate to provide a margin of error of 5% at the 95% confidence level. The study used the R programming language to execute regression analysis, resulting in statistical significance for each predictor variable independently on the dependent variable. The findings provided the construction of predictive analytic models and addressed the hypotheses. Lastly, the results included a multiple regression model where analysis of the independent variables found that at F(5, 379) = 4445, p < .000 with an adjusted R2 of 0.983, the model is statistically significant.
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