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.
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)
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 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
omega
, alpha
, and ci.reliability
.
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. 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), 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. 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), 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
# 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); # }