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Related Concept Videos

Ranks01:02

Ranks

236
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...
236
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

180
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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Review and Preview01:10

Review and Preview

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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.
Percentiles are a type of fractile that partition data into...
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Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

317
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
317
Kendall's Tau Test01:16

Kendall's Tau Test

664
Kendall's tau test, also known as the Kendall rank coefficient test, is a nonparametric method for assessing association between two variables. This test is particularly useful for identifying significant correlations when the distributions of the sample and population are unknown. Developed in 1938 by the British statistician Sir Maurice George Kendall, the tau coefficient (denoted as τ) serves as a rank correlation coefficient, with values ranging from -1 to +1.
A τ value...
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Ratio Level of Measurement00:54

Ratio Level of Measurement

17.8K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
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MRET: Modified Recursive Elimination Technique for ranking author assessment parameters.

Ghulam Mustafa1, Abid Rauf1, Muhammad Tanvir Afzal2

  • 1Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan.

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|June 13, 2024
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Summary
This summary is machine-generated.

This study ranks author assessment metrics using deep learning, finding the normalized h index most important. Trigonometric Mean (TM) excelled in statistical analysis for identifying award winners in mathematics.

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Area of Science:

  • Bibliometrics and Scientometrics
  • Artificial Intelligence in Research Evaluation
  • Scholarly Impact Analysis

Background:

  • Evaluating scholarly contributions requires effective author assessment metrics.
  • Existing metrics like publication and citation counts, and the h-index, have limitations.
  • Prioritizing the most impactful metrics is crucial amidst a plethora of options.

Purpose of the Study:

  • To classify and rank author assessment parameters using deep learning.
  • To identify the most important parameters for evaluating scholarly impact.
  • To analyze correlations and dependencies among 64 distinct author assessment metrics.

Main Methods:

  • Utilized Multi-Layer Perceptron (MLP) classifier for pattern recognition and ranking.
  • Employed a modified recursive elimination technique to assign importance scores to parameters.
  • Conducted statistical analysis combining parameters using seven methods (e.g., arithmetic mean, harmonic mean).
  • Analyzed award winner occurrences within ranked parameter combinations.

Main Results:

  • The normalized h index received the highest importance score among 64 parameters.
  • Trigonometric Mean (TM) outperformed other statistical models in the analysis.
  • Combining metrics like M Quotient and FG index with others yielded excellent results for identifying awardees.

Conclusions:

  • Deep learning effectively ranks author assessment parameters.
  • The normalized h index is a highly effective metric for scholarly impact.
  • Trigonometric Mean and specific index combinations show promise for future research evaluations.