TestGardener (version 3.3.6)

scorePerformance: Calculate mean squared error and bias for a set of score index values from simulated data.

Description

After the simulated data matrices have been analyzed, prepare the objects necessary for the performance plots produced by functions RMSEbias1.plot and RMSEbias2.plot.

Usage

scorePerformance(dataList, simList)

Value

A named list containing these objects:

sumscr:

A matrix with row dimension nchcemat, the number of population score index values and column dimension nsample, the number of simulated samples.

chcemat:

An nchcemat by nsample matrix of estimated score index values.

mu:

An nchcemat by nsample matrix of estimated expected score values.

al:

An nchcemat by nsample matrix of estimated test information curve values.

chcepop:

A vector of population score index values.

mupop:

A vector of expected scores computed from the population score index values.

infopop:

A vector of test information values computed from the population score index values.

n:

The number of questions.

Qvec:

The five marker percentile values.

Arguments

dataList

A list that contains the objects needed to analyse the test or rating scale with the following fields:

chcemat:

A matrix of response data with N rows and n columns where N is the number of examinees or respondents and n is the number of items. Entries in the matrices are the indices of the options chosen. Column i of chcemat is expected to contain only the integers 1,...,noption.

optList:

A list vector containing the numerical score values assigned to the options for this question.

key:

If the data are from a test of the multiple choices type where the right answer is scored 1 and the wrong answers 0, this is a numeric vector of length n containing the indices the right answers. Otherwise, it is NULL.

Sfd:

An fd object for the defining the surprisal curves.

noption:

A numeric vector of length n containing the numbers of options for each item.

nbin:

The number of bins for binning the data.

scrrng:

A vector of length 2 containing the limits of observed sum scores.

scrfine:

A fine mesh of test score values for plotting.

scrvec:

A vector of length N containing the examinee or respondent sum scores.

itemvec:

A vector of length n containing the question or item sum scores.

percntrnk:

A vector length N containing the sum score percentile ranks.

chcematQnt:

A numeric vector of length 2*nbin + 1 containing the bin boundaries alternating with the bin centers. These are initially defined as seq(0,100,len=2*nbin+1).

Sdim:

The total dimension of the surprisal scores.

PcntMarkers:

The marker percentages for plotting: 5, 25, 50, 75 and 95.

simList

A named list containing these objects:

sumscr:

A matrix with row dimension nchcemat, the number of population score index values and column dimension nsample, the number of simulated samples.

chcemat:

An nchcemat by nsample of estimated score index values.

mu:

An nchcemat by nsample of estimated expected score values.

al:

An nchcemat by nsample of estimated test information curve values.

thepop:

A vector of population score index values.

mupop:

A vector of expected scores computed from the population score index values.

alpop:

A vector of test information values computed from the population score index values.

n:

The number of questions.

Qvec:

The five marker percentile values.

References

Ramsay, J. O., Li J. and Wiberg, M. (2020) Full information optimal scoring. Journal of Educational and Behavioral Statistics, 45, 297-315.

Ramsay, J. O., Li J. and Wiberg, M. (2020) Better rating scale scores with information-based psychometrics. Psych, 2, 347-360.

See Also

dataSimulation