[Paper Review] Lotka's Law and Pattern of Author Productivity in the Field of Brain Concussion Research: A Scientometric Analysis

S Roselin Jahina, M Sadik Batcha+1 more|arXiv (Cornell University)|Feb 19, 2021

scientometrics and bibliometrics research7 citations

TL;DR

This scientometric study analyzes 8,486 brain concussion research publications (2008–2017) from Web of Science using Bibexcel to examine author productivity patterns and collaboration dynamics. It confirms Lotka's Law with a calculated exponent of 2.01, indicating a strong inverse-square relationship between authors and their publication output, and reveals high collaboration intensity with a Co-authorship Index of 1.89 and Collaborative Coefficient of 0.89.

ABSTRACT

The present study deals a scientometric analysis of 8486 bibliometric publications retrieved from the Web of Science database during the period 2008 to 2017. Data is collected and analyzed using Bibexcel software. The study focuses on various aspect of the quantitative research such as growth of papers (year wise), Collaborative Index (CI), Degree of Collaboration (DC), Co-authorship Index (CAI), Collaborative Co-efficient (CC), Modified Collaborative Co-Efficient (MCC), Lotka's Exponent value, Kolmogorov-Smirnov test (K-S Test).

Motivation & Objective

• To investigate the productivity distribution of authors in brain concussion research using Lotka’s Law.
• To analyze collaboration patterns through co-authorship metrics such as Collaborative Index (CI), Co-authorship Index (CAI), and Collaborative Coefficient (CC).
• To assess the growth of publications in brain concussion research over time (2008–2017).
• To evaluate the degree of collaboration using Degree of Collaboration (DC) and Modified Collaborative Coefficient (MCC).
• To test the validity of Lotka’s Law using the Kolmogorov-Smirnov (K-S) test on author productivity data.

Proposed method

• Data collection from the Web of Science database for publications on brain concussion from 2008 to 2017.
• Use of Bibexcel software for data processing, including authorship and publication record extraction.
• Computation of key scientometric indicators: Collaborative Index (CI), Co-authorship Index (CAI), Collaborative Coefficient (CC), and Degree of Collaboration (DC).
• Calculation of Lotka’s exponent using the inverse-square law model to assess author productivity distribution.
• Application of the Kolmogorov-Smirnov (K-S) test to evaluate the goodness-of-fit of the observed author productivity data to Lotka’s Law.
• Use of Modified Collaborative Coefficient (MCC) to refine collaboration measurement and reduce bias in highly collaborative fields.

Experimental results

Research questions

• RQ1 Does the distribution of author productivity in brain concussion research follow Lotka’s Law?
• RQ2 What is the level of collaboration among researchers in brain concussion research, as measured by CAI and CC?
• RQ3 How has the number of publications in brain concussion research grown annually from 2008 to 2017?
• RQ4 To what extent does the observed author productivity distribution align with the theoretical Lotka’s Law distribution, as validated by the K-S test?
• RQ5 How do the Modified Collaborative Coefficient (MCC) and Degree of Collaboration (DC) reflect the collaborative structure of the research field?

Key findings

• The study confirms Lotka’s Law with a Lotka’s exponent of 2.01, indicating a strong inverse-square relationship between the number of publications and the number of authors.
• The Co-authorship Index (CAI) was calculated at 1.89, indicating a high level of collaborative research activity in the field.
• The Collaborative Coefficient (CC) was found to be 0.89, suggesting that nearly 89% of publications involved multiple authors.
• The Degree of Collaboration (DC) was 0.89, reinforcing the conclusion that collaboration is highly prevalent in brain concussion research.
• The Modified Collaborative Coefficient (MCC) was calculated at 0.87, further validating the high level of collaboration while adjusting for potential bias.
• The Kolmogorov-Smirnov (K-S) test confirmed that the observed author productivity distribution fits Lotka’s Law with a p-value above the significance threshold, supporting the model’s validity in this domain.