# scaleStructure

##### scaleStructure

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

```
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 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

##### 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

##### See Also

`omega`

, `alpha`

, and `ci.reliability`

.

##### Examples

```
# NOT RUN {
# }
# 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);
# }
# NOT RUN {
# }
```

*Documentation reproduced from package userfriendlyscience, version 0.7.2, License: GPL (>= 3)*