split_coefs: Calculate a bivariate coefficient for each split-half replication

Description

Calculates a bivariate coefficient across participants for each split-half replication and returns their values calculated across replications. ds should be a data frame as returned by by_split: 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. For averaging internal consistency coefficients, see Feldt and Charter (2006).

Usage

split_coefs(ds, fn_coef, ...)

Arguments

(data frame) a data frame with columns split, score_1, and score_2

fn_coef

(function) a function that calculates a bivariate coefficient.

...

Additional arguments passed to fn_coef

Value

Coefficients per split calculated via fn_coef.

References

Feldt, L. S., & Charter, R. A. (2006). Averaging internal consistency reliability coefficients. Educational and Psychological Measurement, 66(2), 215-227. 10.1177/0013164404273947

Examples

Run this code

# NOT RUN {
# 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(score_1 = numeric(), score_2 = numeric(), replication = 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))
}
names(ds_splits) = c("score_1", "score_2", "replication")
# Pearson correlations
split_coefs(ds_splits, cor)
# Spearman-brown corrected Pearson correlations
split_coefs(ds_splits, spearman_brown)
# Flanagan-Rulon coefficient
split_coefs(ds_splits, flanagan_rulon)
# Angoff-Feldt coefficient
split_coefs(ds_splits, angoff_feldt)
# Spearman-Brown corrected ICCs
split_coefs(
  ds_splits,
  spearman_brown,
  short_icc,
  type = "ICC1",
  lmer = FALSE
)
# }