scorePerformance: Calculate mean squared error and bias for a set of score...
In TestGardener: Information Analysis for Test and Rating Scale Data
View source: R/scorePerformance.R
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.
- SfdPar:
An fdPar 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.
See Also
dataSimulation
TestGardener documentation built on Nov. 24, 2023, 5:08 p.m.
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View source: R/scorePerformance.R
| 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:
|
simList |
A named list containing these objects:
|
Value
A named list containing these objects:
- sumscr:
A matrix with row dimension
nchcemat, the number of population score index values and column dimensionnsample, the number of simulated samples.- chcemat:
An
nchcematbynsamplematrix of estimated score index values.- mu:
An
nchcematbynsamplematrix of estimated expected score values.- al:
An
nchcematbynsamplematrix 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.
See Also
dataSimulation
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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