Compute various measures of internal consistencies for tests or item-scales of questionnaires.

## Usage

`item_intercor(x, method = c("pearson", "spearman", "kendall"))`

## Arguments

- x
A matrix as returned by the

`cor()`

-function, or a data frame with items (e.g. from a test or questionnaire).- method
Correlation computation method. May be one of

`"pearson"`

(default),`"spearman"`

or`"kendall"`

. You may use initial letter only.

## Details

This function calculates a mean inter-item-correlation, i.e. a
correlation matrix of `x`

will be computed (unless `x`

is already a matrix
as returned by the `cor()`

function) and the mean of the sum of all items'
correlation values is returned. Requires either a data frame or a computed
`cor()`

object.

"Ideally, the average inter-item correlation for a set of items should be
between 0.20 and 0.40, suggesting that while the items are reasonably
homogeneous, they do contain sufficiently unique variance so as to not be
isomorphic with each other. When values are lower than 0.20, then the items
may not be representative of the same content domain. If values are higher
than 0.40, the items may be only capturing a small bandwidth of the
construct." *(Piedmont 2014)*

## References

Piedmont RL. 2014. Inter-item Correlations. In: Michalos AC (eds) Encyclopedia of Quality of Life and Well-Being Research. Dordrecht: Springer, 3303-3304. doi:10.1007/978-94-007-0753-5_1493