composite_d_scalar | R Documentation |
This function estimates the d value of a composite of X variables, given the mean d value of the individual X values and the mean correlation among those variables.
composite_d_scalar( mean_d, mean_intercor, k_vars, p = 0.5, partial_intercor = FALSE )
mean_d |
The mean standardized mean differences associated with variables in the composite to be formed. |
mean_intercor |
The mean correlation among the variables in the composite. |
k_vars |
The number of variables in the composite. |
p |
The proportion of cases in one of the two groups used the compute the standardized mean differences. |
partial_intercor |
Logical scalar determining whether the |
There are two different methods available for computing such a composite, one that uses the partial intercorrelation among the X variables (i.e., the average within-group correlation) and one that uses the overall correlation among the X variables (i.e., the total or mixture correlation across groups).
If a partial correlation is provided for the interrelationships among variables, the following formula is used to estimate the composite d value:
d_composite = (mean_d * k_vars) / sqrt(mean_intercor * k_vars^2 + (1 - mean_intercor) * k_vars)
where d_composite is the composite d value, mean_d is the mean d value, mean_intercor is the mean intercorrelation among the variables in the composite, and k is the number of variables in the composite.
Otherwise, the composite d value is computed by converting the mean d value to a correlation, computing the composite correlation (see composite_r_scalar
for formula), and transforming that composite back into the d metric.
The estimated standardized mean difference associated with the composite variable.
Rosenthal, R., & Rubin, D. B. (1986). Meta-analytic procedures for combining studies with multiple effect sizes. Psychological Bulletin, 99(3), 400–406.
composite_d_scalar(mean_d = 1, mean_intercor = .7, k_vars = 2, p = .5)
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