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Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox

Overview of attention for article published in Emerging Themes in Epidemiology, January 2008
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  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 147)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

policy
1 policy source
twitter
26 tweeters
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
164 Dimensions

Readers on

mendeley
282 Mendeley
citeulike
4 CiteULike
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Title
Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox
Published in
Emerging Themes in Epidemiology, January 2008
DOI 10.1186/1742-7622-5-2
Pubmed ID
Authors

Yu-Kang Tu, David Gunnell, Mark S Gilthorpe

Abstract

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon--the reversal paradox--depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results.

Twitter Demographics

The data shown below were collected from the profiles of 26 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 282 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 4%
Germany 4 1%
United Kingdom 4 1%
Netherlands 3 1%
Canada 2 <1%
Belgium 2 <1%
Czechia 1 <1%
Sweden 1 <1%
Colombia 1 <1%
Other 4 1%
Unknown 249 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 27%
Researcher 59 21%
Student > Master 24 9%
Professor > Associate Professor 22 8%
Professor 17 6%
Other 64 23%
Unknown 21 7%
Readers by discipline Count As %
Psychology 72 26%
Medicine and Dentistry 56 20%
Social Sciences 26 9%
Agricultural and Biological Sciences 23 8%
Mathematics 20 7%
Other 39 14%
Unknown 46 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2022.
All research outputs
#1,305,312
of 21,934,632 outputs
Outputs from Emerging Themes in Epidemiology
#13
of 147 outputs
Outputs of similar age
#10,709
of 249,881 outputs
Outputs of similar age from Emerging Themes in Epidemiology
#1
of 1 outputs
Altmetric has tracked 21,934,632 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 147 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 249,881 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them