predicted.validity {psych}

Find the predicted validities of a set of scales based on item statistics

Description

The validity of a scale varies as a function of the number of items in the scale, their average intercorrelation, and their average validity. The asymptotic limit of a scales validity for any particular criterion is just the average validity divided by the square root of the average within scale item correlation. predicted.validity will find the predicted validity for a set of scales (defined by a keys.list) and the average item validity for various criteria.

The function will find (and report) scale reliabilities (using reliability) and average item validities (using item.validity)

Usage

predicted.validity(x, criteria, keys, scale.rel = NULL, item.val = NULL)
item.validity(x,criteria,keys) 
validityItem(x,criteria,keys)

Arguments

x

A data set

criteria

Variables to predict from the scales

keys

A keys.list that defines the scales

scale.rel

If not specified, these will be found. Otherwise, this is the output from reliability.

item.val

If not specified, the average item validities for each scale will be found. Otherwise use the output from item.validity

Details

When predicting criteria from a set of items formed into scales, the validity of the scale (that is, the correlations of the scale with each criteria) is a function of the average item validity (r_y), the average intercorrelation of the items in the scale (r_x), and the number of items in the scale (n). The limit of validity is r_y/sqrt(r_x).

Criteria will differ in their predictability from a set of scales. These asymptotic values may be used to help the decision on which scales to develop further.

Value

predicted

The predicted validities given the scales specified

item.validities

The average item validities for each scale with each criterion

scale.reliabilities

The various statistics reported by the reliability function

asymptotic

A matrix of the asymptotic validities

Author(s)

William Revelle

References

Revelle, William. (in prep) An introduction to psychometric theory with applications in R. Springer. Working draft available at https://personality-project.org/r/book/

Revelle, W. and Condon, D.M. (2019) Reliability from alpha to omega: A tutorial. Psychological Assessment, 31, 12, 1395-1411. https://doi.org/10.1037/pas0000754. https://osf.io/preprints/psyarxiv/2y3w9 Preprint available from PsyArxiv

See Also

reliability, scoreItems, scoreFast

Examples

pred.bfi <- predicted.validity(bfi[,1:25], bfi[,26:28], bfi.keys)
pred.bfi