composite_r_scalar | R Documentation |
This function estimates the correlation between a set of X variables and a set of Y variables using a scalar formula.
composite_r_scalar(
mean_rxy,
k_vars_x = NULL,
mean_intercor_x = NULL,
k_vars_y = NULL,
mean_intercor_y = NULL
)
mean_rxy |
Mean correlation between sets of X and Y variables. |
k_vars_x |
Number of X variables. |
mean_intercor_x |
Mean correlation among X variables. |
k_vars_y |
Number of Y variables. |
mean_intercor_y |
Mean correlation among Y variables. |
The formula to estimate a correlation between one composite variable and one external variable is:
\rho_{Xy}=\frac{\bar{\rho}_{x_{i}y}}{\sqrt{\frac{1}{k_{x}}+\frac{k_{x}-1}{k_{x}}\bar{\rho}_{x_{i}x_{j}}}}
and the formula to estimate the correlation between two composite variables is:
\rho_{XY}=\frac{\bar{\rho}_{x_{i}y_{j}}}{\sqrt{\frac{1}{k_{x}}+\frac{k-1}{k_{x}}\bar{\rho}_{x_{i}x_{j}}}\sqrt{\frac{1}{k_{y}}+\frac{k_{y}-1}{k_{y}}\bar{\rho}_{y_{i}y_{j}}}}
where \bar{\rho}_{x_{i}y}
and \bar{\rho}_{x_{i}y{j}}
are mean correlations between the x variables and the y variable(s),
\bar{\rho}_{x_{i}x_{j}}
is the mean correlation among x variables,
\bar{\rho}_{y_{i}y_{j}}
is the mean correlation among y variables,
{k}_{x}
is the number of x variables, and {k}_{y}
is the number of y variables.
A vector of composite correlations
Ghiselli, E. E., Campbell, J. P., & Zedeck, S. (1981). Measurement theory for the behavioral sciences. San Francisco, CA: Freeman. p. 163-164.
Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings (3rd ed.). Thousand Oaks, CA: Sage. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.4135/9781483398105")}. pp. 441 - 447.
## Composite correlation between 4 variables and an outside variable with which
## the four variables correlate .3 on average:
composite_r_scalar(mean_rxy = .3, k_vars_x = 4, mean_intercor_x = .4)
## Correlation between two composites:
composite_r_scalar(mean_rxy = .3, k_vars_x = 2, mean_intercor_x = .5,
k_vars_y = 2, mean_intercor_y = .2)
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