# correct_r_coarseness: Correct correlations for scale coarseness In psychmeta: Psychometric Meta-Analysis Toolkit

 correct_r_coarseness R Documentation

## Correct correlations for scale coarseness

### Description

Corrects correlations for scale coarseness.

### Usage

```correct_r_coarseness(
r,
kx = NULL,
ky = NULL,
n = NULL,
dist_x = "norm",
dist_y = "norm",
bin_value_x = c("median", "mean", "index"),
bin_value_y = c("median", "mean", "index"),
width_x = 3,
width_y = 3,
lbound_x = NULL,
ubound_x = NULL,
lbound_y = NULL,
ubound_y = NULL,
index_values_x = NULL,
index_values_y = NULL
)
```

### Arguments

 `r` Observed correlation. `kx, ky` Number of scale points used to measure the x and y variables. Set to NULL to treat as continuously measured. `n` Optional sample size. `dist_x, dist_y` Assumed latent distribution of the x and y variables. `bin_value_x, bin_value_y` Are the scale points used to measure the of the x and y variables assumed to represent bin medians, means, or index values? `width_x, width_y` For symmetrically distributed variables, how many standard deviations above/below the latent mean should be be used for the latent variable range to make the correction? (Note: Setting `width` > 3 produces erratic results.) The latent variable range can alternatively be set using `lbound` and `ubound`. `lbound_x, lbound_y` What lower bound of the range for the latent x and y variables should be used to make the correction? (Note: For normally distributed variables, setting `lbound` < -3 produces erratic results.) `ubound_x, ubound_y` What upper bound of the range for the latent x and y variables should be used to make the correction? (Note: For normally distributed variables, setting `ubound` > 3 produces erratic results.) `index_values_x, index_values_y` Optional. If `bin_value` = "index", the bin index values. If unspecified, values 1:k are used.

### Value

Vector of correlations corrected for scale coarseness (if `n` is supplied, corrected error variance and adjusted sample size is also reported).

### References

Aguinis, H., Pierce, C. A., & Culpepper, S. A. (2009). Scale coarseness as a methodological artifact: Correcting correlation coefficients attenuated from using coarse scales. Organizational Research Methods, 12(4), 623–652. doi: 10.1177/1094428108318065

Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings (3rd ed.). Sage. doi: 10.4135/9781483398105. pp. 287-288.

Peters, C. C., & Van Voorhis, W. R. (1940). Statistical procedures and their mathematical bases. New York, NY: Mcgraw-Hill. doi: 10.1037/13596-000. pp. 393–399.

### Examples

```correct_r_coarseness(r = .35, kx = 5, ky = 4, n = 100)
correct_r_coarseness(r = .35, kx = 5, n = 100)
correct_r_coarseness(r = .35, kx = 5, ky = 4, n = 100, dist_x="unif", dist_y="norm")
```

psychmeta documentation built on Aug. 26, 2022, 5:14 p.m.