split_ci: Generate bootstrap replicates of a coefficient split-half...

In splithalfr: Estimate Split-Half Reliabilities

Generate bootstrap replicates of a coefficient split-half reliability estimate by sampling participants

Description

Generates bootstrap replicates via boot. The parameter ds should be a data frame as returned by by_split: Each unique value of the column participant is considered a independent sample of the target population. For each unique value of the column split in ds, it selects the corresponding rows in ds, and passes the values in the columns score_1 and score_2 as the first and second argument to fn_coef. Any row in ds for which score_1 or score_2 is NA is pairwise removed before passing the data to fn_coef. Any coefficient that is NA is removed before passing the data to fn_summary.

Usage

split_ci(
  ds,
  fn_coef,
  fn_average,
  bootstrap_replications = 1000,
  parallel = "snow",
  ncpus = detectCores(),
  ...
)

Arguments

ds	(data frame) a data frame with columns split, score_1, and score_2
fn_coef	(function) a function that calculates a bivariate (reliability) coefficient
fn_average	(function) a function that calculates an average across coefficients
bootstrap_replications	(integer) number of bootstrap replications
parallel	(character) Type of parallel processing (if any) used for bootstrapping. See boot
ncpus	(character) Number of cores for parallel processing. See boot
...	Additional arguments passed to boot

Details

For a practical example, see one of the vignettes for getting started with the splithalfr. Also, note that the boot package supports parallel processing via the parameters 'parallel' and 'ncpus'.

For averaging internal consistency coefficients, see Feldt and Charter (2006). For more information about bias-corrected and accelerated bootstrap confidence intervals, see Efron (1987).

Value

Confidence interval

References

Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American statistical Association, 82(397), 171-185. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.1987.10478410")}

Feldt, L. S., & Charter, R. A. (2006). Averaging internal consistency reliability coefficients. Educational and Psychological Measurement, 66(2), 215-227. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0013164404273947")}

See Also

Other split aggregation functions: split_coefs()

Examples

# Import boot library
library(boot)
# Generate five splits with scores that are correlated 0.00, 0.25, 0.5, 0.75, and 1.00
library(MASS)
ds_splits = data.frame(V1 = numeric(), V2 = numeric(), split = numeric())
for (r in 0:4) {
  vars = mvrnorm(10, mu = c(0, 0), Sigma = matrix(c(10, 3, 3, 2), ncol = 2), empirical = FALSE)
  ds_splits = rbind(ds_splits, cbind(vars, r, 1 : 10))
}
names(ds_splits) = c("score_1", "score_2", "replication", "participant")
# Conduct bootstrap
bootstrap_result <- split_ci(ds_splits, cor, mean, parallel = "no")
# Get boosted and accelerated confidence intervals
print(boot.ci(bootstrap_result, type="bca"))

splithalfr documentation built on June 8, 2025, 10 a.m.