convert_es: Convert effect sizes

Description Usage Arguments Value References Examples

View source: R/convert_es.R

Description

\loadmathjax

This function converts a variety of effect sizes to correlations, Cohen's \mjseqnd values, or common language effect sizes, and calculates sampling error variances and effective sample sizes.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
convert_es(
  es,
  input_es = c("r", "d", "delta", "g", "t", "p.t", "F", "p.F", "chisq", "p.chisq",
    "or", "lor", "Fisherz", "A", "auc", "cles"),
  output_es = c("r", "d", "A", "auc", "cles"),
  n1 = NULL,
  n2 = NULL,
  df1 = NULL,
  df2 = NULL,
  sd1 = NULL,
  sd2 = NULL,
  tails = 2
)

Arguments

es

Vector of effect sizes to convert.

input_es

Scalar. Metric of input effect sizes. Currently supports correlations, Cohen's \mjseqnd, independent samples \mjseqnt values (or their \mjseqnp values), two-group one-way ANOVA \mjseqnF values (or their \mjseqnp values), 1-df \mjeqn\chi^2\chi-squared values (or their \mjseqnp values), odds ratios, log odds ratios, Fisher z, and the common language effect size (CLES, A, AUC).

output_es

Scalar. Metric of output effect sizes. Currently supports correlations, Cohen's \mjseqnd values, and common language effect sizes (CLES, A, AUC).

n1

Vector of total sample sizes or sample sizes of group 1 of the two groups being contrasted.

n2

Vector of sample sizes of group 2 of the two groups being contrasted.

df1

Vector of input test statistic degrees of freedom (for \mjseqnt and \mjeqn\chi^2\chi-squared) or between-groups degree of freedom (for \mjseqnF).

df2

Vector of input test statistic within-group degrees of freedom (for \mjseqnF).

sd1

Vector of pooled (within-group) standard deviations or standard deviations of group 1 of the two groups being contrasted.

sd2

Vector of standard deviations of group 2 of the two groups being contrasted.

tails

Vector of the tails for \mjseqnp values when input_es = "p.t". Can be 2 (default) or 1.

Value

A data frame of class es with variables:

r, d, A

The converted effect sizes

n_effective

The effective total sample size

n

The total number of cases (original sample size)

n1, n2

If applicable, subgroup sample sizes

var_e

The estimated sampling error variance

References

Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine, 19(22), 3127–3131. doi: 10.1002/1097-0258(20001130)19:22<3127::AID-SIM784>3.0.CO;2-M

Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Sage.

Ruscio, J. (2008). A probability-based measure of effect size: Robustness to base rates and other factors. Psychological Methods, 13(1), 19–30. doi: 10.1037/1082-989X.13.1.19

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

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
convert_es(es = 1,  input_es="d", output_es="r", n1=100)
convert_es(es = 1, input_es="d", output_es="r", n1=50, n2 = 50)
convert_es(es = .2, input_es="r", output_es="d",  n1=100, n2=150)
convert_es(es = -1.3, input_es="t", output_es="r", n1 = 100, n2 = 140)
convert_es(es = 10.3, input_es="F", output_es="d", n1 = 100, n2 = 150)
convert_es(es = 1.3, input_es="chisq", output_es="r", n1 = 100, n2 = 100)
convert_es(es = .021, input_es="p.chisq", output_es="d", n1 = 100, n2 = 100)
convert_es(es = 4.37, input_es="or", output_es="r", n1=100, n2=100)
convert_es(es = 4.37, input_es="or", output_es="d", n1=100, n2=100)
convert_es(es = 1.47, input_es="lor", output_es="r", n1=100, n2=100)
convert_es(es = 1.47, input_es="lor", output_es="d", n1=100, n2=100)

Example output

-----------------------------------------------------  psychmeta version 2.4.2  --

Please report any bugs to github.com/psychmeta/psychmeta/issues
or issues@psychmeta.com

We work hard to produce these open-source tools for the R community.
Please cite psychmeta when you use it in your research:
  Dahlke, J. A., & Wiernik, B. M. (2019). psychmeta: An R package for
    psychometric meta-analysis. Applied Psychological Measurement, 43(5), 415-416.
    https://doi.org/10.1177/0146621618795933

---------------------------------------------------------------  Version check  --Version check not run.
Assumed equal group sizes.
r values converted from d values
-----------------------------------------
      r n_effective   n n1 n2   var_e
1 0.447         100 100 50 50 0.00646
r values converted from d values
-----------------------------------------
      r n_effective   n n1 n2   var_e
1 0.447         100 100 50 50 0.00646
d values converted from r values
-----------------------------------------
      d n_effective   n  n1  n2  var_e
1 0.417         250 250 100 150 0.0172
r values converted from t values
-----------------------------------------
       r n_effective   n  n1  n2   var_e
1 -0.084         240 240 100 140 0.00413
F values converted to effect sizes. Check effect direction coding.
d values converted from F values
-----------------------------------------
      d n_effective   n  n1  n2  var_e
1 0.414         250 250 100 150 0.0171
r values converted from chisq values
-----------------------------------------
       r n_effective   n  n1  n2   var_e
1 0.0806         200 200 100 100 0.00496
p values converted to effect sizes. Check effect direction coding.
d values converted from p.chisq values
-----------------------------------------
      d n_effective   n  n1  n2  var_e
1 0.331         200 200 100 100 0.0205
r values converted from or values
-----------------------------------------
      r n_effective   n  n1  n2  var_e
1 0.377         200 200 100 100 0.0037
d values converted from or values
-----------------------------------------
      d n_effective   n  n1  n2  var_e
1 0.813         200 200 100 100 0.0219
r values converted from lor values
-----------------------------------------
      r n_effective   n  n1  n2   var_e
1 0.376         200 200 100 100 0.00371
d values converted from lor values
-----------------------------------------
     d n_effective   n  n1  n2  var_e
1 0.81         200 200 100 100 0.0219

psychmeta documentation built on June 1, 2021, 9:13 a.m.