ralertingp {qcv}

Inferential test for ralerting-CV

Description

Computes a p value for the ralerting-CV effect size

Usage

ralertingp(actr, predr, iter)

Arguments

actr

vector of actual validity correlations

predr

vector of predicted validity correlations (in same order as actr)

iter

the number of randomization samples to be generated

Details

An inferential test for ralerting-CV, not presented in the original Westen and Rosenthal (2003). The inferential test is based upon randomization procedures. That is, given a set of predicted correlations and a set of actual correlations, ralertingp() randomly pairs values from the two sets, computes an ralerting-CV value, and repeats this process many times (as determined by the user). The actual ralerting-CV value is then compared to the distribution of ralerting-CV values derived from the randomization process. The proportion of values from that distribution that are greater than the actual ralerting-CV value is then taken as a p value.

Important: When entering values for the "actr" and "predr" arguments, order them identically. For example, if the "actr" values are ordered in terms of c(criterion A, criterion B, criterion C), then the values in "predr" should be placed in that same order. Otherwise results will be incorrect.

For relatively small values of k (the number of criterion variables), it is recommended to increase iter (the number of randomization samples. For low-k situations, a small number of randomization samples can produce unstable p values. A larger number of randomization samples would produce more stable results.

It is possible that the randomization process produces no values that are greater than the actual ralerting-CV value. In such cases, ralertingp() reports the p value as < 1/iter.

Examples

actrIM  <- c(.46, .13, -.24, -.03, .12, .03, .39, .06, .51, .08, .24, .66)
predrIM <- c(.58, .24, -.04, .06, -.04, .18, .36, .08, .64, .56, .36, .56)
ralertingp(actr=actrIM, predr=predrIM, iter=1000)