Confidence intervals for point-biserial correlations

Source: R/statpsych2.R

Computes confidence intervals for two types of population point-biserial correlations. One type uses a weighted average of the group variances and is appropriate for nonexperimental designs with simple random sampling (but not stratified random sampling). The other type uses an unweighted average of the group variances and is appropriate for experimental designs. Equality of variances is not assumed for either type.

ci.pbcor(alpha, m1, m2, sd1, sd2, n1, n2)

Arguments

alpha

alpha level for 1-alpha confidence

m1

estimated mean for group 1

m2

estimated mean for group 2

sd1

estimated standard deviation for group 1

sd2

estimated standard deviation for group 2

n1

sample size for group 1

n2

sample size for group 2

Value

Returns a 2-row matrix. The columns are:

  • Estimate - estimated point-biserial correlation

  • SE - standard error

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

References

Bonett DG (2020). “Point-biserial correlation: Interval estimation, hypothesis testing, meta-analysis, and sample size determination.” British Journal of Mathematical and Statistical Psychology, 73(S1), 113--144. ISSN 0007-1102, doi:10.1111/bmsp.12189 .

Examples

ci.pbcor(.05, 28.32, 21.48, 3.81, 3.09, 40, 40)
#>              Estimate         SE        LL        UL
#> Weighted:   0.7065799 0.04890959 0.5885458 0.7854471
#> Unweighted: 0.7020871 0.05018596 0.5808366 0.7828948

# Should return:
#              Estimate         SE        LL        UL
# Weighted:   0.7065799 0.04890959 0.5885458 0.7854471
# Unweighted: 0.7020871 0.05018596 0.5808366 0.7828948