# Effektstärken-Klassen effect size classes

Diese Seite wurde seit 11 Jahren inhaltlich nicht mehr aktualisiert.
Unter Umständen ist sie nicht mehr aktuell.

## BiblioMap

## Synonyme

Effektstärken-Klassen, effect size classes

## Bemerkungen

Cohen’s cut-offs provide a good basis for interpreting effect size and for resolving disputes about the importance of one’s results.

Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Interpreting effects auf Seite 40In his authoritative Statistical Power Analysis for the Behavioral Sciences, Cohen (1988) outlined a number of criteria for gauging small, medium, and large effect sizes estimated using different statistical procedures.

Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Interpreting effects Cohen himself was not unaware of the "many dangers" associated with benchmarking effect sizes, noting that the conventions were devised "with much diffidence, qualifications, and invitations not to employ them if possible" (1988: 12, 532).

Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Interpreting effects auf Seite 42The fact that they are used at all - given that they have no raison d’etre beyond Cohen’s own judgment - speaks volumes about the inherent difficulties researchers have in drawing conclusions about the real-world significance of their results.

Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Interpreting effects auf Seite 42Despite these advantages the interpretation of results using Cohen’s criteria remains a controversial practice. Noted scholars such as Gene Glass, one of the developers of meta-analysis, have vigorously argued against classifying effects into "t-shirt sizes" of small, medium, and large.

Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Interpreting effects auf Seite 41There is no wisdom whatsoever in attempting to associate regions of the effect size metric with descriptive adjectives such as “small,” “moderate,” “large,” and the like. Dissociated from a context
of decision and comparative value, there is little inherent value to an effect size of 3.5 or .2. Depending on what benefits can be achieved at what cost, an effect size of 2.0 might be “poor” and one of .1 might be “good.”

Von Gene V. Glass, Barry McGaw, Mary Lee Smith im Buch Meta-analysis in social research (1981) auf Seite 104Reliance on arbitrary benchmarks such as Cohen’s hinders the researcher from thinking about what the results really mean. Thompson (2008: 258) takes the view that Cohen’s cut-offs are "not generally useful" and notes the risk that scholars may interpret these numbers with the same mindless rigidity that has been applied to the p = .05 level in statistical significance testing. Shaver (1993: 303) agrees: "Substituting sanctified effect size conventions for the sanctified .05 level of statistical significance is not progress."

Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Interpreting effects auf Seite 42Cohen’s effect size classes have two selling points. First, they are easy to grasp. You just compare your numbers with his thresholds to get a ready-made interpretation of your result. Second, although they are arbitrary, they are sufficiently grounded in logic for Cohen to hope that his cut-offs “will be found to be reasonable by reasonable people" (1988: 13). In deciding the boundaries for the three size classes, Cohen began by defining a medium effect as one that is “visible to the naked eye of the careful observer" (Cohen 1992: 156). To use his example, a medium effect is equivalent to the difference in height between fourteen- and eighteen-year-old girls, which is about one inch. He then defined a small effect as one that is less than a medium effect, but greater than a trivial effect. Small effects are equivalent to the height difference between fifteen- and sixteen-year-old girls, which is about half an inch. Finally, a large effect was defined as one that was as far above a medium effect as a small one was below it. In this case, a large effect is equivalent to the height difference between thirteen- and eighteen-year-old girls, which is just over an inch and a half.

Von Paul D. Ellis im Buch The Essential Guide to Effect Sizes (2010) im Text Interpreting effects auf Seite 41Effect sizes seen in the social sciences are oftentimes very small
(Rosnow & Rosenthal, 2003). This has led to difficulties in their
interpretation. There is no agreement on what magnitude of effect
is necessary to establish practical significance. Cohen (1992) of fers the value of r .1, as a cut-off for “small” effects (which
would indicate only a 1% overlap in variance between two variables).
However, Cohen did not anchor his recommendations
across effect sizes; as such, his recommendations for r and d
ultimately differ in magnitude when translated from one to another.
For instance, Cohen suggests that r .3 and d .5 each
indicate a cut-off for moderate effects, yet r .3 is not the
equivalent of d .5. Other scholars suggest a minimum of r .2
(Franzblau, 1958; Lipsey, 1998) or .3 (Hinkle, Weirsma, & Jurs,
1988). In the current article, all effect size recommendations,
where possible, are anchored to a minimum of r .2, for practical
significance (Franzblau, 1958; Lipsey, 1998). These readily convert
from r to d for instance, without altering the interpretation.
Note that this is a suggested minimum not a guarantee that observed
effect sizes larger than r .2 are practically significant.
Such cut-offs are merely guidelines, and should not be applied
rigidly (Cohen, 1992; Snyder & Lawson, 1993; Thompson, 2002).

Von Christopher J. Ferguson im Text An Effect Size Primer (2009) ## Verwandte Objeke

Verwandte Begriffe (co-word occurance) |

## Häufig co-zitierte Personen

Jacob

Cohen

Cohen

## Statistisches Begriffsnetz

## Zitationsgraph

## 5 Erwähnungen

- Meta-analysis in social research (Gene V. Glass, Barry McGaw, Mary Lee Smith) (1981)
- Statistical Power Analysis for the Behavioral Sciences (Jacob Cohen) (1988)
- Visible Learning - A Synthesis of Over 800 Meta-Analyses Relating to Achievement (John Hattie) (2009)
- 2. The nature of evidence - a synthesis of meta-analysis

- An Effect Size Primer - A Guide for Clinicians and Researchers (Christopher J. Ferguson) (2009)
- The Essential Guide to Effect Sizes - Statistical Power, Meta-Analysis, and the Interpretation of Research Results (Paul D. Ellis) (2010)