The influence of outliers on metric studies of information: an analysis of univariate data
DOI:
https://doi.org/10.19132/1808-5245240.216-235Keywords:
Outliers, Univariados, Bibliometria, Assimetria, Análise exploratória de dadosAbstract
This paper presents a new formula for detecting outliers through Exploratory Data Analysis, while taking data asymmetry into account. The effect of removing outliers from the original dataset was also assessed. The new formula was applied on three datasets published in the literature on metric studies of information. The first dataset presented five lower outliers. The average of aggregate data conveyed a false impression that 40 universities, from a total of 49, were above average. The removal of the five lower outliers leads to a new average in which only 22 universities were above average. In the second dataset, there were five lower outliers and one upper outlier. In this case, the upper outlier eventually weaken the effect of the lower outliers. In the third dataset, five upper outliers and one lower outlier are detected. The average of aggregate data revealed that 10 universities were above average. Removing the six outliers from the original dataset, it was found that 28 universities were above the new average score. For the three datasets analyzed, the assessment demonstrated the effect of the outliers on the interval estimation (statistical inference): the removal of outliers generated a mean and standard deviation that were more representative of the sample analyzed. Therefore, became evident how outliers could influence results and conclusions in metric studies of the information. However, the formula for outliers’ detection is open for future research.
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Copyright (c) 2019 Luís Fernando Maia Lima, Alexandre Masson Maroldi, Dávilla Vieira Odízio da Silva, Carlos Roberto Massao Hayashi, Maria Cristina Piumbato Innocentini Hayashi
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