psychmeta (version 2.7.0)

convert_es: Convert effect sizes

Description

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

Usage

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
)

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

Arguments

es

Vector of effect sizes to convert.

input_es

Scalar. Metric of input effect sizes. Currently supports correlations, Cohen's \(d\), independent samples \(t\) values (or their \(p\) values), two-group one-way ANOVA \(F\) values (or their \(p\) values), 1-df \(\chi^{2}\) values (or their \(p\) 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 \(d\) 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 \(t\) and \(\chi^{2}\)) or between-groups degree of freedom (for \(F\)).

df2

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

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 \(p\) values when input_es = "p.t". Can be 2 (default) or 1.

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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.4135/9781483398105")

Examples

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)