Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 3 4 5 6 7 8  scaleStructure(dat=NULL, items = 'all', digits = 2, ci = TRUE,
interval.type="normaltheory", 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="normaltheory", conf.level=.95,
silent=FALSE, samples=1000, bootstrapSeed = NULL,
omega.psych = TRUE, poly = TRUE)

dat 
A dataframe containing the items in the scale. All variables in this
dataframe will be used if items = 'all'. If 
items 
If not 'all', this should be a character vector with the names of the variables in the dataframe that represent items in the scale. 
digits 
Number of digits to use in the presentation of the results. 
ci 
Whether to compute confidence intervals as well. If true, the method
specified in 
interval.type 
Method to use when computing confidence intervals. The list of methods
is explained in 
conf.level 
The confidence of the confidence intervals. 
silent 
If computing confidence intervals, the user is warned that it may take a
while, unless 
samples 
The number of samples to compute for the bootstrapping of the confidence intervals. 
bootstrapSeed 
The seed to use for the bootstrapping  setting this seed makes it possible to replicate the exact same intervals, which is useful for publications. 
omega.psych 
Whether to also compute the interval estimate for omega using the

poly 
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 
Input specified when calling the function 
intermediate 
Intermediate values and objects computed to get to the final results 
output 
Values of reliability / internal consistency measures, with as most notable elements: 
output$dat 
A dataframe with the most important outcomes 
output$omega 
Point estimate for omega 
output$glb 
Point estimate for the Greatest Lower Bound 
output$alpha 
Point estimate for Cronbach's alpha 
output$coefficientH 
Coefficient H 
output$omega.ci 
Confidence interval for omega 
output$alpha.ci 
Confidence interval for Cronbach's alpha 
GjaltJorn Peters and Daniel McNeish (University of North Carolina, Chapel Hill, US).
Maintainer: GjaltJorn Peters <[email protected]>
Crutzen, R., & Peters, G.J. Y. (2015). Scale quality: alpha is an inadequate estimate and factoranalytic evidence is needed first of all. Health Psychology Review. http://dx.doi.org/10.1080/17437199.2015.1124240
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), 399412. doi:10.1111/bjop.12046
Eisinga, R., Grotenhuis, M. Te, & Pelzer, B. (2013). The reliability of a twoitem scale: Pearson, Cronbach, or SpearmanBrown? International Journal of Public Health, 58(4), 63742. doi:10.1007/s0003801204163
Gadermann, A. M., Guhn, M., Zumbo, B. D., & Columbia, B. (2012). Estimating ordinal reliability for Likerttype and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research & Evaluation, 17(3), 112.
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), 5669. http://ehps.net/ehp/index.php/contents/article/download/ehp.v16.i2.p56/1
Revelle, W., & Zinbarg, R. E. (2009). Coefficients Alpha, Beta, Omega, and the glb: Comments on Sijtsma. Psychometrika, 74(1), 145154. doi:10.1007/s113360089102z
Sijtsma, K. (2009). On the Use, the Misuse, and the Very Limited Usefulness of Cronbach's Alpha. Psychometrika, 74(1), 107120. doi:10.1007/s1133600891010
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), 123133. doi:10.1007/s1133600309747
omega
, alpha
, and ci.reliability
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  ## 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 tauequivalence).
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);
## End(Not run)

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