Availability bias Availability bias
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Synonyme
Availability bias, Verfügbarkeitsverzerrung
Definitionen
Als availability bias - oder Verfügbarkeitsverzerrung - wird die Gefahr bezeichnet, dass eine Meta-Analyse durch leichter verfügbare Studien verzerrt wird, d.h. weniger gut verfügbare Studien nicht in die Meta-Analyse einbezogen werden und die leichter verfügbaren Studien sich bezüglich den untersuchten Effekten von den schwieriger verfügbaren Studien unterscheiden.
Von Beat Döbeli Honegger, erfasst im Biblionetz am 31.03.2013Bemerkungen
A meta-analysis will ideally include all the relevant research on an effect. The exclusion of some relevant research can lead to an availability bias. An availability bias arises when effect size estimates obtained from studies which are readily available to the reviewer differ from those estimates reported in studies which are less accessible. An availability bias is seldom intentional and usually arises as a result of a reporting bias, the file drawer problem, a publication bias, and the Tower of Babel bias.
Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Minimizing bias in meta-analysis auf Seite 116Another way to quantify the bias arising from the incomplete representation of relevant research is to calculate the "fail-safe N." The fail-safe N is the minimum number of additional studies with conflicting evidence thatwould be needed to overturn the conclusion reached in the review. Conflicting evidence is usually defined as a null result. If the meta-analysis has generated a statistically significant finding, the fail-safe N is the number of excluded studies averaging null results that would be needed to render that finding nonsignificant (Rosenthal 1979). The fail-safe N is directly related to the size of the effect and the number of studies (k) combined to estimate it in the meta-analysis. For example, if the results of fourteen studies were combined to yield a statistically significant mean effect size of r = .15, p = .018, it would require the addition of only nine further studies averaging a null effect to render this result statistically nonsignificant. If we could accept the possibility that there are at least nine "no effect" results buried in filing cabinets or published in obscure non-English journals, then we should be skeptical of the meta-analytic conclusion. However, if the fourteen studies returned a mean effect size of r = .30, then the fail-safe number would be a much higher seventy-eight studies. Thus, the fail-safe N describes the tolerance level of a result. The larger the N, the more tolerant the result will be of excluded null results.
Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Minimizing bias in meta-analysis auf Seite 122Verwandte Objeke
Verwandte Begriffe (co-word occurance) | self-serving biasself-serving bias(0.06), VerlustaversionLoss aversion(0.05), SelbstüberschätzungOverconfidence effect(0.04) |
Häufig co-zitierte Personen
Niccolò
Machiavelli
Machiavelli
Thomas
Hobbes
Hobbes
Abhijit
Banerjee
Banerjee
Esther
Duflo
Duflo
Statistisches Begriffsnetz
Zitationsgraph
8 Erwähnungen
- Judgment under Uncertainty - Heuristics and Biases (Daniel Kahneman, Paul Slovic, Amos Tversky) (1982)
- Worst-Case Scenarios (Cass R. Sunstein) (2009)
- The Essential Guide to Effect Sizes - Statistical Power, Meta-Analysis, and the Interpretation of Research Results (Paul D. Ellis) (2010)
- Why Nudge? - The Politics of Libertarian Paternalism (Cass R. Sunstein) (2014)
- The 25 Cognitive Biases - Uncovering The Myth of Rational Thinking (Charles Holm) (2015)
- What works - Wie Verhaltensdesign die Gleichstellung revolutionieren kann (Iris Bohnet) (2017)
- Im Grunde gut - Eine neue Geschichte der Menschheit (Rutger Bregman) (2019)
- Die Kunst des digitalen Lebens - Wie Sie auf News verzichten und die Informationsflut meistern (Rolf Dobelli) (2019)