Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review
Testing University Rankings Statistically: Why this Perhaps is not such a Good Idea after All. Some Reflections on Statistical Power, Effect Size, Random Sampling and Imaginary Populations. / Schneider, Jesper Wiborg.
Proceedings of 17th International Conference on Science and Technology Indicators. ed. / Éric Archambault; Yves Gingras; Vincent Larivière . 2012. p. 719-732.Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Testing University Rankings Statistically: Why this Perhaps is not such a Good Idea after All. Some Reflections on Statistical Power, Effect Size, Random Sampling and Imaginary Populations
AU - Schneider, Jesper Wiborg
N1 - Conference code: 17th
PY - 2012/9
Y1 - 2012/9
N2 - In this paper we discuss and question the use of statistical significance tests in relation to university rankings as recently suggested. We outline the assumptions behind and interpretations of statistical significance tests and relate this to examples from the recent SCImago Institutions Ranking. By use of statistical power analyses and demonstration of effect sizes, we emphasize that importance of empirical findings lies in “differences that make a difference” and not statistical significance tests per se. Finally we discuss the crucial assumption of randomness and question the presumption that randomness is present in the university ranking data. We conclude that the application of statistical significance tests in relation to university rankings, as recently advocated, is problematic and can be misleading.
AB - In this paper we discuss and question the use of statistical significance tests in relation to university rankings as recently suggested. We outline the assumptions behind and interpretations of statistical significance tests and relate this to examples from the recent SCImago Institutions Ranking. By use of statistical power analyses and demonstration of effect sizes, we emphasize that importance of empirical findings lies in “differences that make a difference” and not statistical significance tests per se. Finally we discuss the crucial assumption of randomness and question the presumption that randomness is present in the university ranking data. We conclude that the application of statistical significance tests in relation to university rankings, as recently advocated, is problematic and can be misleading.
KW - statistical significance tests
KW - controversy
KW - university rankings
KW - effect sizes
KW - statistcial power
KW - scientometrics
KW - indicators
M3 - Article in proceedings
SP - 719
EP - 732
BT - Proceedings of 17th International Conference on Science and Technology Indicators
A2 - Archambault, Éric
A2 - Gingras, Yves
A2 - Larivière , Vincent
T2 - International Conference on Science and Technology Indicators
Y2 - 5 September 2012 through 8 September 2012
ER -
Department of Political Science
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