The scaleStructure function (which was originally called scaleReliability)
computes a number of measures to assess scale reliability and internal
consistency. Note that to compute omega, the `MBESS`

and/or the
`psych`

packages need to be installed, which are suggested packages and
therefore should be installed separately (i.e. won't be installed
automatically).

```
scaleStructure(
data = NULL,
items = "all",
digits = 2,
ci = TRUE,
interval.type = "normal-theory",
conf.level = 0.95,
silent = FALSE,
samples = 1000,
bootstrapSeed = NULL,
omega.psych = TRUE,
omega.psych_nfactors = 3,
omega.psych_flip = TRUE,
poly = TRUE,
suppressSuggestedPkgsMsg = FALSE,
headingLevel = 3
)
# S3 method for scaleStructure
print(x, digits = x$input$digits, ...)
scaleStructure_partial(
x,
headingLevel = x$input$headingLevel,
quiet = TRUE,
echoPartial = FALSE,
partialFile = NULL,
...
)
# S3 method for scaleStructure
knit_print(
x,
headingLevel = x$input$headingLevel,
quiet = TRUE,
echoPartial = FALSE,
partialFile = NULL,
...
)
```

- data
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. This requires the suggested MBESS package, which has to be installed separately. 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 the help file for

`ci.reliability`

in MBESS. 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.- omega.psych_nfactors
The number of factor to use in the factor analysis when computing Omega. The default in

`psych::omega()`

is 3; to obtain the same results as in jamovi's "Reliability", set this to 1.- omega.psych_flip
Whether to let

`psych`

automatically flip items with negative correlations. The default in`psych::omega()`

is`TRUE`

; to obtain the same results as in jamovi's "Reliability", set this to`FALSE`

.- poly
Whether to compute ordinal measures (if the items have sufficiently few categories).

- suppressSuggestedPkgsMsg
Whether to suppress the message about the suggested

`MBESS`

and`psych`

packages.- headingLevel
The level of the Markdown heading to provide; basically the number of hashes ('

`#`

') to prepend to the headings.- x
The object to print

- ...
Any additional arguments for the default print function.

- quiet
Passed on to

`knitr::knit()`

whether it should b chatty (`FALSE`

) or quiet (`TRUE`

).- echoPartial
Whether to show the executed code in the R Markdown partial (

`TRUE`

) or not (`FALSE`

).- partialFile
This can be used to specify a custom partial file. The file will have object

`x`

available, which is the result of a call to`scaleStructure()`

.

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

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.

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.

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.*
doi: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. doi:10.7275/n560-j767

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.
doi:10.31234/osf.io/h47fv

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

```
if (FALSE) {
### (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)
ufs::scaleStructure(dat=exampleData, ci=FALSE);
### Use a selection of three variables (without confidence
### intervals to save time
ufs::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
ufs::scaleStructure(ordinalExampleData, ci=FALSE);
}
```