item.omega | R Documentation |
This function computes point estimate and confidence interval for the coefficient omega (McDonald, 1978), hierarchical coefficient omega (Kelley & Pornprasertmanit, 2016), and categorical coefficient omega (Green & Yang, 2009) along with standardized factor loadings and omega if item deleted. By default, the function computes coefficient omega based on maximum likelihood parameter (ML) estimates using full information maximum likelihood (FIML) method in the presence of missing data.
item.omega(data, ..., rescov = NULL, type = c("omega", "hierarch", "categ"),
exclude = NULL, std = FALSE,
estimator = c("ML", "GLS", "WLS", "DWLS", "ULS", "PML"),
missing = c("listwise", "pairwise", "fiml"),
print = c("all", "omega", "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 three items are needed for computing coefficient omega |
... |
an expression indicating the variable names in |
rescov |
a character vector or a list of character vectors for
specifying residual covariances when computing coefficient
omega, e.g. |
type |
a character string indicating the type of omega 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 omega 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 omega is computed by conducting a confirmatory factor analysis based
on the congeneric 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).
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 categorical omega 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 omega and item statistics, pairwise deletion
item.omega(dat)
# Example 1b: Coefficient omega and item statistics, listwise deletion
item.omega(dat, missing = "listwise")
# Example 2: Coefficient omega and item statistics after excluding item3
item.omega(dat, exclude = "item3")
# Example 3a: Coefficient omega with a residual covariance
# and item statistics
item.omega(dat, rescov = c("item1", "item2"))
# Example 3b: Coefficient omega with residual covariances
# and item statistics
item.omega(dat, rescov = list(c("item1", "item2"), c("item1", "item3")))
# Example 4: Ordinal coefficient omega and item statistics
item.omega(dat, type = "categ")
# Example 6: Summary of the CFA model used to compute coefficient omega
lavaan::summary(item.omega(dat, output = FALSE)$model.fit,
fit.measures = TRUE, standardized = TRUE)
# Example 7a: Write Results into a text file
item.omega(dat, write = "Omega.txt")
# Example 7b: Write Results into a Excel file
item.omega(dat, write = "Omega.xlsx")
## End(Not run)
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