dataSimulation {TestGardener}

Simulation Based Estimates of Error Variation of Score Index Estimates

Description

Estimate sum score,s score index values index and test information values bias and mean squared errors using simulated data.

Usage

  dataSimulation(dataList, parmList, nsample = 1000)

Arguments

dataList

The list object set up by function make_dataList.

parmList

The list object containing objects computed by function Analyze.

nsample

The number of simulated samples.

Value

A named list object containing objects produced from analyzing the simulations, one set for each simulation:

sumscr:

Sum score estimates

index:

Score index estimates

mu:

Expected sum score estimates

info:

Total arc length estimates

index.pop:

True or population score index values

mu.pop:

Expected sum score population values

info.pop:

Total test length population values

n:

Number of items

nindex:

Number of index values

indfine:

Fine mesh over score index range

Qvec:

Five marker percentages: 5, 25, 50, 75 and 95

Author(s)

Juan Li and James Ramsay

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

scorePerformance