scaleStructure: scaleStructure In userfriendlyscience: Quantitative Analysis Made Accessible

Description

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.

Usage

 ```1 2 3 4 5 6 7 8``` ```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) ```

Arguments

 `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).

Details

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.

Value

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

Author(s)

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

Maintainer: Gjalt-Jorn Peters <[email protected]>

References

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`.
 ``` 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 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) ```