# scorePerformance: Calculate mean squared error and bias for a set of score... In TestGardener: Information Analysis for Test and Rating Scale Data

 scorePerformance R Documentation

## 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)
``````

### 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.

### 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.

### 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.

`dataSimulation`