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Related Concept Videos

Ranks01:02

Ranks

586
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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Percentile01:18

Percentile

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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. Low percentiles always correspond to lower data values. High percentiles always correspond to higher data values.Percentiles divide ordered data into hundredths. To score in the...
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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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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Ordinal Level of Measurement00:55

Ordinal Level of Measurement

24.0K
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.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
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Quartile01:15

Quartile

7.0K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
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Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA
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Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

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Ranking games.

Margit Osterloh1, Bruno S Frey2

  • 1Zeppelin University, Friedrichshafen, Germany CREMA-Center for Research in Economics, Management and the Arts, Zurich, Switzerland.

Evaluation Review
|August 6, 2014
PubMed
Summary
This summary is machine-generated.

Academic bibliometric rankings shape university governance and careers. However, they may foster a "taste for publication" over a genuine "taste for science," with potentially negative consequences.

Keywords:
academic governancemotivationrankingsselectionsocialization

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Area of Science:

  • Bibliometrics
  • Academic Governance
  • Scholarly Publishing

Background:

  • Bibliometric rankings significantly influence academic governance and career progression.
  • Current systems heavily rely on quantitative metrics for evaluation.

Purpose of the Study:

  • To analyze the incentives driving the use and supply of academic rankings.
  • To investigate the potential unintended consequences of these rankings.

Main Methods:

  • An analytical approach was employed.
  • Incentives for both users and suppliers of rankings were examined at individual and aggregate levels.

Main Results:

  • Rankings can lead to a shift from a "taste for science" to a "taste for publication."
  • The underlying assumptions supporting the utility of rankings are increasingly challenged by recent research.

Conclusions:

  • Alternative evaluation methods include socialization, selection, self-evaluations, and awards.
  • Fostering controversial discourse is crucial for meaningful scientific advancement.