Er zijn verschillende overzichten te vinden, maar een handige tabel staat op http://imaging.mrc-cbu.***.ac.uk/statswiki/FAQ/effectSize. Gemakshalve heb ik die hier even gepaste:
The scales of magnitude are taken from Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. The scales of magnitude for partial $\omega^\text{2}$ are taken from Table 2.2 of Murphy and Myors (2004).
There is also a table of effect size magnitudes at the back of Kotrlik JW and Williams HA (2003). An overview of commonly used effect sizes in psychology is given by Vacha-Haase and Thompson (2004).
Kraemer and Thiemann (1987, p.54 and 55) use the same effect size values (which they call delta) for both intra-class correlations and Pearson correlations. This implies the below rules of thumb from Cohen (1988) for magnitudes of effect sizes for Pearson correlations could also be used for intra-class correlations. It should be noted, however, that the intra-class correlation is computed from a repeated measures ANOVA whose usual effect size (given below) is partial eta-squared. In addition, Shrout and Fleiss (1979) discuss different types of intra-class correlation coefficient and how their magnitudes can differ.
The general rules of thumb given by Cohen and Miles & Shevlin (2001) are for eta-squared, which uses the total sum of squares in the denominator, but these would arguably apply more to partial eta-squared than to eta-squared. This is because partial eta-squared in factorial ANOVA arguably more closely approximates what eta-squared would have been for the factor had it been a one-way ANOVA and it is presumably a one-way ANOVA which gave rise to Cohen's rules of thumb.
Effect Size
|
Use
|
Small
|
Medium
|
Large
|
Correlation
|
|
0.1
|
0.3
|
0.5
|
$\eta^2$
|
one-way anova (regression)
|
0.01
|
0.06
|
0.14
|
$\eta^2$
|
Anova
|
0.02
|
0.13
|
0.26
|
omega-squared
|
Anova; See Field (2013)
|
0.01
|
0.06
|
0.14
|
Multivariate eta-squared
|
one-way MANOVA
|
0.01
|
0.06
|
0.14
|
Cohen's f
|
one-way an(c)ova (regression)
|
0.10
|
0.25
|
0.40
|
$\eta^2$
|
Multiple regression
|
0.02
|
0.13
|
0.26
|
$\kappa^2$
|
Mediation analysis
|
0.01
|
0.09
|
0.25
|
Cohen's f
|
Multiple Regression
|
0.14
|
0.39
|
0.59
|
Cohen's d
|
t-tests
|
0.2
|
0.5
|
0.8
|
Cohen's $\omega$
|
chi-square
|
0.1
|
0.3
|
0.5
|
Odds Ratios
|
2 by 2 tables
|
1.5
|
3.5
|
9.0
|
Average Spearman rho
|
Friedman test
|
0.1
|
0.3
|
0.5
|
Also:Haddock et al (1998) state that $\sqrt{3/\pi}$ multiplied by the log of the odds ratio is a standardised difference equivalent to Cohen's d.