item.alpha | R Documentation |
This function computes point estimate and confidence interval for the coefficient
alpha (aka Cronbach's alpha), hierarchical alpha, and ordinal alpha (aka categorical
alpha) along with standardized factor loadings and alpha if item deleted. By
default, the function computes coefficient alpha based on unweighted least
squares (ULS) parameter estimates using pairwise deletion in the presence of
missing data that provides equivalent results compared to the formula-based
coefficient alpha computed by using e.g. the alpha
function in the
psych package by William Revelle (2025).
item.alpha(data, ..., rescov = NULL, type = c("alpha", "hierarch", "categ"),
exclude = NULL, std = FALSE,
estimator = c("ML", "GLS", "WLS", "DWLS", "ULS", "PML"),
missing = c("listwise", "pairwise", "fiml"),
print = c("all", "alpha", "item"), digits = 2, conf.level = 0.95,
as.na = NULL, write = NULL, append = TRUE, check = TRUE,
output = TRUE)
data |
a data frame. Note that at least two items are needed for computing coefficient alpha |
... |
an expression indicating the variable names in |
rescov |
a character vector or a list of character vectors for
specifying residual covariances when computing coefficient
alpha, e.g. |
type |
a character string indicating the type of alpha to be computed,
i.e., |
exclude |
a character vector indicating items to be excluded from the analysis. |
std |
logical: if |
estimator |
a character string indicating the estimator to be used
(see 'Details' in the |
missing |
a character string indicating how to deal with missing data.
(see 'Details' in the |
print |
a character vector indicating which results to show, i.e.
|
digits |
an integer value indicating the number of decimal places to be used for displaying alpha and standardized factor loadings. |
conf.level |
a numeric value between 0 and 1 indicating the confidence level of the interval. |
as.na |
a numeric vector indicating user-defined missing values,
i.e. these values are converted to |
write |
a character string naming a file for writing the output into
either a text file with file extension |
append |
logical: if |
check |
logical: if |
output |
logical: if |
Coefficient alpha is computed by conducting a confirmatory factor analysis based
on the essentially tau-equivalent measurement model (Graham, 2006) using the cfa()
function in the lavaan package by Yves Rosseel (2019).
Approximate confidence intervals are computed using the procedure by Feldt,
Woodruff and Salih (1987). Note that there are at least 10 other procedures
for computing the confidence interval (see Kelley and Pornprasertmanit, 2016),
which are implemented in the ci.reliability()
function in the
MBESSS package by Ken Kelley (2019)
Ordinal coefficient alpha was introduced by Zumbo, Gadermann and Zeisser (2007). Note that Chalmers (2018) highlighted that the categorical coefficient alpha should be interpreted only as a hypothetical estimate of an alternative reliability, whereby a test's ordinal categorical response options have be modified to include an infinite number of ordinal response options and concludes that coefficient alpha should not be reported as a measure of a test's reliability. However, Zumbo and Kroc (2019) argued that Chalmers' critique of categorical coefficient alpha is unfounded and that categorical coefficient alpha may be the most appropriate quantifier of reliability when using Likert-type measurement to study a latent continuous random variable.
Returns an object of class misty.object
, which is a list with following
entries:
call |
function call |
type |
type of analysis |
data |
data frame used for the current analysis |
args |
specification of function arguments |
model.fit |
fitted lavaan object ( |
result |
list with result tables, i.e., |
Computation of the hierarchical and ordinal alpha is based on the
ci.reliability()
function in the MBESS package by Ken Kelley
(2019).
Takuya Yanagida takuya.yanagida@univie.ac.at
Chalmers, R. P. (2018). On misconceptions and the limited usefulness of ordinal alpha. Educational and Psychological Measurement, 78, 1056-1071. https://doi.org/10.1177/0013164417727036
Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334. https://doi.org/10.1007/BF02310555
Cronbach, L.J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement, 64, 391-418. https://doi.org/10.1177/0013164404266386
Feldt, L. S., Woodruff, D. J., & Salih, F. A. (1987). Statistical inference for coefficient alpha. Applied Psychological Measurement, 11 93-103. https://doi.org/10.1177/014662168701100107
Graham, J. M. (2006). Congeneric and (essentially) tau-equivalent estimates of score reliability: What they are and how to use them. Educational and Psychological Measurement, 66(6), 930–944. https://doi.org/10.1177/0013164406288165
Kelley, K., & Pornprasertmanit, S. (2016). Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures. Psychological Methods, 21, 69-92. https://doi.org/10.1037/a0040086.
Ken Kelley (2019). MBESS: The MBESS R Package. R package version 4.6.0. https://CRAN.R-project.org/package=MBESS
Revelle, W. (2025). psych: Procedures for psychological, psychometric, and personality research. Northwestern University, Evanston, Illinois. R package version 2.5.3, https://CRAN.R-project.org/package=psych.
Zumbo, B. D., & Kroc, E. (2019). A measurement is a choice and Stevens' scales of measurement do not help make it: A response to Chalmers. Educational and Psychological Measurement, 79, 1184-1197. https://doi.org/10.1177/0013164419844305
Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta for Likert rating scales. Journal of Modern Applied Statistical Methods, 6, 21-29. https://doi.org/10.22237/jmasm/1177992180
item.omega
, item.cfa
, item.invar
,
item.reverse
, item.scores
, write.result
## Not run:
dat <- data.frame(item1 = c(3, NA, 3, 4, 1, 2, 4, 2), item2 = c(5, 3, 3, 2, 2, 1, 3, 1),
item3 = c(4, 2, 4, 2, 1, 3, 4, 1), item4 = c(4, 1, 2, 2, 1, 3, 4, 3))
# Example 1a: Coefficient alpha and item statistics, pairwise deletion
item.alpha(dat)
# Example 1b: Coefficient alpha and item statistics, listwise deletion
item.alpha(dat, missing = "listwise")
# Example 1c: Coefficient alpha and item statistics, FIML
item.alpha(dat, estimator = "ML", missing = "fiml")
# Example 2: Coefficient alpha and item statistics after excluding item3
item.alpha(dat, exclude = "item3")
# Example 3a: Coefficient alpha with a residual covariance
# and item statistics
item.alpha(dat, rescov = c("item1", "item2"))
# Example 3b: Coefficient alpha with residual covariances
# and item statistics
item.alpha(dat, rescov = list(c("item1", "item2"), c("item1", "item3")))
# Example 4: Ordinal coefficient alpha and item statistics
item.alpha(dat, type = "categ")
# Example 6: Summary of the CFA model used to compute coefficient alpha
lavaan::summary(item.alpha(dat, output = FALSE)$model.fit,
fit.measures = TRUE, standardized = TRUE)
# Example 7a: Write Results into a text file
item.alpha(dat, write = "Alpha.txt")
# Example 7b: Write Results into a Excel file
item.alpha(dat, write = "Alpha.xlsx")
## End(Not run)
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