cronbachs {quest}

Cronbach's Alpha for Multiple Sets of Variables/Items

Description

cronbachs computes Cronbach's alpha for multiple sets of variables/items as an estimate of reliability for multiple scores. There are three different options for confidence intervals. Missing data can be handled by either pairwise deletion (use = "pairwise.complete.obs") or listwise deletion (use = "complete.obs"). cronbachs is a wrapper for the alpha function in the psych package.

Usage

cronbachs(
  data,
  vrb.nm.list,
  ci.type = "delta",
  level = 0.95,
  use = "pairwise.complete.obs",
  stats = c("average_r", "nvrb"),
  R = 200L,
  boot.ci.type = "perc"
)

Arguments

data

data.frame of data.

vrb.nm.list

list of character vectors specifying the sets of variables/items. Each element of vrb.nm.list provides the colnames of data for that set of variables/items.

ci.type

character vector of length 1 specifying the type of confidence interval to compute. The options are 1) "classic" = the Feldt et al. (1987) procedure using only the mean covariance, 2) "delta" = the Duhhacheck & Iacobucci (2004) procedure using the delta method of the covariance matrix, or 3) "boot" = bootstrapped confidence intervals with the method specified by boot.ci.type.

level

double vector of length 1 with a value between 0 and 1 specifying what confidence level to use.

use

character vector of length 1 specifying how to handle missing data when computing the covariances. The options are 1) "pairwise.complete.obs", 2) "complete.obs", 3) "na.or.complete", 4) "all.obs", or 5) "everything". See details of cov.

stats

character vector specifying the additional statistical information you could like related to cronbach's alpha. Options are: 1) "std.alpha" = cronbach's alpha of the standardized variables/items, 2) "G6(smc)" = Guttman's Lambda 6 reliability, 3) "average_r" = mean correlation between the variables/items, 4) "median_r" = median correlation between the variables/items, 5) "mean" = mean of the the scores from averaging the variables/items together, 6) "sd" = standard deviation of the scores from averaging the variables/items together, 7) "nvrb" = number of variables/items. The default is "average_r" and "nvrb".

R

integer vector of length 1 specifying the number of bootstrapped resamples to do. Only used when ci.type = "boot".

boot.ci.type

character vector of length 1 specifying the type of bootstrapped confidence interval to compute. The options are 1) "perc" for the regular percentile method, 2) "bca" for bias-corrected and accelerated percentile method, 3) "norm" for the normal method that uses the bootstrapped standard error to construct symmetrical confidence intervals with the classic formula around the bias-corrected estimate, and 4) "basic" for the basic method. Note, "stud" for the studentized method is NOT an option. See boot.ci as well as confint2.boot for details.

Details

When ci.type = "classic" the confidence interval is based on the mean covariance. It is the same as the confidence interval used by alpha.ci (Feldt, Woodruff, & Salih, 1987). When ci.type = "delta" the confidence interval is based on the delta method of the covariance matrix. It is based on the standard error returned by alpha (Duhachek & Iacobucci, 2004).

Value

data.frame containing the following columns:

est

Cronbach's alpha itself

se

standard error for Cronbach's alpha

lwr

lower bound of the confidence interval of Cronbach's alpha

upr

upper bound for the confidence interval of Cronbach's alpha

,

???

any statistics requested via the stats argument

References

Feldt, L. S., Woodruff, D. J., & Salih, F. A. (1987). Statistical inference for coefficient alpha. Applied Psychological Measurement (11) 93-103.

Duhachek, A. and Iacobucci, D. (2004). Alpha's standard error (ase): An accurate and precise confidence interval estimate. Journal of Applied Psychology, 89(5):792-808.

See Also

cronbach composites

Examples

dat0 <- psych::bfi
dat1 <- str2str::pick(x = dat0, val = c("A1","C4","C5","E1","E2","O2","O5",
   "gender","education","age"), not = TRUE, nm = TRUE)
vrb_nm_list <- lapply(X = str2str::sn(c("E","N","C","A","O")), FUN = function(nm) {
   str2str::pick(x = names(dat1), val = nm, pat = TRUE)})
cronbachs(data = dat1, vrb.nm.list = vrb_nm_list, ci.type = "classic")
cronbachs(data = dat1, vrb.nm.list = vrb_nm_list, ci.type = "delta")
cronbachs(data = dat1, vrb.nm.list = vrb_nm_list, ci.type = "boot")
suppressMessages(cronbachs(data = attitude, vrb.nm.list =
   list(names(attitude)))) # also works with only one set of variables/items