Description Usage Arguments Details References Examples
Converts Pearson's r (computed with a continuous X and Y) to Cohen's d for use in meta-analysis. The resulting Cohen's d represents the estimated increase in standardized Y that is associated with a delta-unit increase in X.
1 |
r |
Pearson's correlation |
sx |
Sample standard deviation of X |
delta |
Contrast in X for which to compute Cohen's d, specified in raw units of X (not standard deviations). |
N |
Sample size used to estimate |
Ns |
Sample size used to estimate |
sx.known |
Is |
To preserve the sign of the effect size, the code takes the absolute value of delta
. The standard error
estimate assumes that X is approximately normal and that N
is large.
Mathur MB & VanderWeele TJ (2019). A simple, interpretable conversion from Pearson's correlation to Cohen's d for meta-analysis. Epidemiology.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # d for a 1-unit vs. a 2-unit increase in X
r_to_d( r = 0.5,
sx = 2,
delta = 1,
N = 100 )
r_to_d( r = 0.5,
sx = 2,
delta = 2,
N = 100 )
# d when sx is estimated in the same vs. a smaller sample
# point estimate will be the same, but inference will be a little
# less precise in second case
r_to_d( r = -0.3,
sx = 2,
delta = 2,
N = 300,
Ns = 300 )
r_to_d( r = -0.3,
sx = 2,
delta = 2,
N = 300,
Ns = 30 )
|
d se lo hi
1 0.2886751 0.06210718 0.1669473 0.410403
d se lo hi
1 0.5773503 0.1242144 0.3338946 0.8208059
d se lo hi
1 -0.3144855 0.06217226 -0.4363408 -0.1926301
d se lo hi
1 -0.3144855 0.07352003 -0.4585821 -0.1703888
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