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Ranks01:02

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Impacts can be classified in various forms, primarily under two subgroups: central impact and oblique impact. A central impact occurs when two objects collide head-on, possessing opposite velocities aligned along the line of impact. Conversely, an oblique impact occurs when two objects collide at an angle, resulting in a modification of both direction and velocity.
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A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Ranking mobility and impact inequality in early academic careers.

Ye Sun1, Fabio Caccioli1,2,3, Giacomo Livan1,2

  • 1Department of Computer Science, University College London, London WC1E 6EA, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|August 14, 2023
PubMed
Summary

Academic career progression is difficult. A bibliometric analysis reveals low mobility in impact rankings for early-career scholars, especially at the top and bottom, though mobility has increased over time.

Keywords:
impact ranking mobilityinequalityscience of science

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Area of Science:

  • Bibliometrics
  • Scientometrics
  • Academic career analysis

Background:

  • Understanding academic career progression is crucial for researchers and institutions.
  • Assessing the ease with which early-career academics can advance within their fields is a key concern.

Purpose of the Study:

  • To analyze the mobility within academic impact rankings across 57 disciplines.
  • To investigate the temporal evolution of this mobility and its relationship with impact inequality.

Main Methods:

  • Comprehensive bibliometric analysis of over 5 million authors (career start 1986-2008).
  • Calibration of a random walk model to historical ranking data.
  • Cohort-based analysis focusing on authors starting in the same year.

Main Results:

  • Remarkably low ranking mobility observed for top- and bottom-ranked authors.
  • This high stability in academic impact rankings persisted throughout the study period.
  • Mobility of impact rankings increased over time, correlating with a decline in impact inequality.

Conclusions:

  • Early-career academics face significant challenges in advancing their impact rankings.
  • Increased ranking mobility over time suggests potential shifts in academic landscape dynamics.
  • Findings have implications for academic policymaking and understanding scholarly opportunities.