Actual correlations

Description

A very simple function, actr() computes the actual correlations between a focal test and a set of criterion variables

Usage

actr(df)

Arguments

Details

The df should contain only scores on the focal test and on the criterion variables. Any additional columns should be deleted. Focal test scores should be in the first column. If you need to reorder columns, you can do so by column number - eg, df <- df[c(1,3,2)] - or by column name - eg, df <- df[c("test", "crit1", "crit2")]

This function uses the "pairwise.complete.obs" option, which handles missing data by pairwise deletion.

Examples

data(motdat)
actr(motdat)

Median r

Description

Computes the median intercorrelation among a set of variables

Usage

medr(df)

Arguments

Details

The data frame should contain only the variables for which a median intercorrelation is desired. For Westen and Rosenthal's (2003) QCV procedure, the data frame should contain only participants' scores on the criterion variables.

This prcoedure handles missing data via the "pairwise.complete.obs" option.

Examples

data(motdat)
motdatc <- motdat[,2:13] #To retain only the criterion variables (dropping the focal test score)
medr(motdatc)

Example data used in Furr & Heuckeroth, on Impression Motivation (IM)

Description

Self-report data from 90 participants, responding to the IM scale and 12 criterion variables.

Usage

data(motdat)

Format

A data frame of 90 rows and 13 variables:

imscale

Impression Motivation

dep

Dependence

mach

Machiavellianism

dis

Distrust

res

Resourcefulness

se

Self-efficacy

ext

Extraversion

agr

Agreeableness

comp

Complexity

psc

Public self-consciousness

sm

Self-monitoring

anx

Anxiety

nfb

Need to belong

Examples

data(motdat)

Plot for Quantifying Construct Validity

Description

Produces a plot of actual and predicted correlations

Usage

plotqcv(actr, predr, labels)

Arguments

Details

Important: When entering values for the three 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" and the labels in "labels" should be placed in that same order.

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)
labelsIM <- c("Dep","Mach","Dis","Res","SE","Ext","Agr","Comp","PSC","SM","Anx","NTB")
plotqcv(actr=actrIM, predr=predrIM, labels=labelsIM)

Printing Quantifying Construct Validity

Description

Prints key results from the qcv() function

Usage

## S3 method for class 'qcv'
print(x, ...)

Arguments

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)
imqcvout <- qcv(n=90, actr=actrIM, predr=predrIM, medr=.075)
print(imqcvout)

Quantifying Construct Validity

Description

Computes key indices related to the Quantifyting Construct Validity (QCV) procedure (Westen & Rosenthal, 2003; see also Furr & Heuckeroth, in prep.)

Usage

qcv(n, actr, predr, medr)

Arguments

Details

This function applies procedures outlined by Westen and Rosenthal (2003; see also Furr & Heuckeroth, in prep).

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 all results will be incorrect.

Note that extreme zcontrast values (e.g., z > 38) create problems. The rcontrast-CV effect size is computed by converting z to p to t to r (see Westen & Rosenthal, Appendix A & B). However, R (and most other packages) doesn't have precision to convert an extremely large z (e.g., Z = 38) to a p value. For such z values, the p is converted to exactly zero. This makes it impossible to obtain t and rcontrast values. The qcv() function handles this by: a) identifying cases where p is initially exactly zero, and b) re-setting p to the smallest value possible, given the machine on which R us running (usually 2.225074e-308). It then proceeds to compute a t and rcontrast from that adjusted p value. It also prints a note stating that the adjustment has been made and that the p, t, and rcontrast values are lower-bound approximations.

Value

ralerting-CV

The ralerting-CV effect size

rcontrast-CV

The rcontrast-CV effect size

zcontrast

The zcontrast value

p

p values associated with Zcontrast

N

Sample size

k

Number of criterion variables

szr

Standard deviation of (z-transformed) actual correlations

medr

Median intercorrelation between the critertion variables

rbarsq

Mean of the squared actual correlations (between focal test and criterion variables)

t

t-value associated with p value

rem

"Remarkablness" of size of contrast

Additional values are returned in a list, but not printed

References

Westen, D., & Rosenthal, R. (2003). Quantifying construct validity: Two simple measures. Journal of Personality and Social Psychology, 84, 608-618.

Furr, R. M., & Heukeroth, S. (In prep).Advancing the Interpretation and Computation of the "Quantifying Construct Validity" Procedure

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)
qcv(n=90, actr=actrIM, predr=predrIM, medr=.075)

Inferential test for ralerting-CV

Description

Computes a p value for the ralerting-CV effect size

Usage

ralertingp(actr, predr, iter)

Arguments

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)