scaleStructure: scaleStructure
In userfriendlyscience: Quantitative Analysis Made Accessible

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.

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 `dat` is `NULL`, a the `getData` function will be called to show the user a dialog to open a file.
`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` is used. When specifying a bootstrapping method, this can take quite a while!
`interval.type`	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.
`conf.level`	The confidence of the confidence intervals.
`silent`	If computing confidence intervals, the user is warned that it may take a while, unless `silent=TRUE`.
`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 `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.
`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

Gjalt-Jorn Peters and Daniel McNeish (University of North Carolina, Chapel Hill, US).

Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

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

omega, alpha, and ci.reliability.

## 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);

## End(Not run)