The scaleStructure function (which was originally called scaleReliability) computes a number of measures to assess scale reliability and internal consistency.

If you use this function in an academic paper, please cite Peters (2014), where the function is introduced, and/or Crutzen & Peters (2015), where the function is discussed from a broader perspective.

utilities, univar
scaleStructure(dat=NULL, items = 'all', digits = 2, ci = TRUE,
               interval.type="normal-theory", conf.level=.95,
               silent=FALSE, samples=1000, bootstrapSeed = NULL,
               omega.psych = TRUE, poly = TRUE)
scaleReliability(dat=NULL, items = 'all', digits = 2, ci = TRUE,
                 interval.type="normal-theory", conf.level=.95,
                 silent=FALSE, samples=1000, bootstrapSeed = NULL,
                 omega.psych = TRUE, poly = TRUE)
A dataframe containing the items in the scale. All variables in this dataframe will be used if items = 'all'. If dat is NULL, a the getData function will be called to show the user a dialog to open a file.
If not 'all', this should be a character vector with the names of the variables in the dataframe that represent items in the scale.
Number of digits to use in the presentation of the results.
Whether to compute confidence intervals as well. If true, the method specified in interval.type is used. When specifying a bootstrapping method, this can take quite a while!
Method to use when computing confidence intervals. The list of methods is explained in ci.reliability. Note that when specifying a bootstrapping method, the method will be set to normal-theory for computing the confidence intervals for the ordinal estimates, because these are based on the polychoric correlation matrix, and raw data is required for bootstrapping.
The confidence of the confidence intervals.
If computing confidence intervals, the user is warned that it may take a while, unless silent=TRUE.
The number of samples to compute for the bootstrapping of the confidence intervals.
The seed to use for the bootstrapping - setting this seed makes it possible to replicate the exact same intervals, which is useful for publications.
Whether to also compute the interval estimate for omega using the omega function in the psych package. The default point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability (because it has more options for computing confidence intervals, not always requiring bootstrapping), whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). The psych estimate usually (perhaps always) results in higher estimates for omega.
Whether to compute ordinal measures (if the items have sufficiently few categories).

This function is basically a wrapper for functions from the psych and MBESS packages that compute measures of reliability and internal consistency. For backwards compatibility, in addition to scaleStructure, scaleReliability can also be used to call this function.


An object with the input and several output variables. Most notably:

Input specified when calling the function
Intermediate values and objects computed to get to the final results
Values of reliability / internal consistency measures, with as most notable elements:
A dataframe with the most important outcomes
Point estimate for omega
Point estimate for the Greatest Lower Bound
Point estimate for Cronbach's alpha
Coefficient H
Confidence interval for omega
Confidence interval for Cronbach's alpha


Crutzen, R., & Peters, G.-J. Y. (2015). Scale quality: alpha is an inadequate estimate and factor-analytic evidence is needed first of all. Health Psychology Review.

Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399-412. doi:10.1111/bjop.12046

Eisinga, R., Grotenhuis, M. Te, & Pelzer, B. (2013). The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? International Journal of Public Health, 58(4), 637-42. doi:10.1007/s00038-012-0416-3

Gadermann, A. M., Guhn, M., Zumbo, B. D., & Columbia, B. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research & Evaluation, 17(3), 1-12.

Peters, G.-J. Y. (2014). The alpha and the omega of scale reliability and validity: why and how to abandon Cronbach's alpha and the route towards more comprehensive assessment of scale quality. European Health Psychologist, 16(2), 56-69.

Revelle, W., & Zinbarg, R. E. (2009). Coefficients Alpha, Beta, Omega, and the glb: Comments on Sijtsma. Psychometrika, 74(1), 145-154. doi:10.1007/s11336-008-9102-z

Sijtsma, K. (2009). On the Use, the Misuse, and the Very Limited Usefulness of Cronbach's Alpha. Psychometrika, 74(1), 107-120. doi:10.1007/s11336-008-9101-0

Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach's alpha, Revelle's beta and McDonald's omega H: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 123-133. doi:10.1007/s11336-003-0974-7

See Also

omega, alpha, and ci.reliability.

  • scaleStructure
  • scaleReliability

## Not run: ------------------------------------
# ### (These examples take a lot of time, so they are not run
# ###  during testing.)
# ### This will prompt the user to select an SPSS file
# scaleStructure();
# ### Load data from simulated dataset testRetestSimData (which
# ### satisfies essential tau-equivalence).
# data(testRetestSimData);
# ### Select some items in the first measurement
# exampleData <- testRetestSimData[2:6];
# ### Use all items (don't order confidence intervals to save time
# ### during automated testing of the example)
# scaleStructure(dat=exampleData, ci=FALSE);
# ### Use a selection of three variables (without confidence
# ### intervals to save time
# scaleStructure(dat=exampleData, items=c('t0_item2', 't0_item3', 't0_item4'),
#                ci=FALSE);
# ### Make the items resemble an ordered categorical (ordinal) scale
# ordinalExampleData <- data.frame(apply(exampleData, 2, cut,
#                                        breaks=5, ordered_result=TRUE,
#                                        labels=as.character(1:5)));
# ### Now we also get estimates assuming the ordinal measurement level
# scaleStructure(ordinalExampleData, ci=FALSE);
## ---------------------------------------------

Documentation reproduced from package userfriendlyscience, version 0.6-1, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.