dataSimulation: Simulation Based Estimates of Error Variation of Score Index...
In TestGardener: Information Analysis for Test and Rating Scale Data

dataSimulation

R Documentation

Simulation Based Estimates of Error Variation of Score Index Estimates

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

  dataSimulation(dataList, parmList, nsample = 1000)

`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.

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

Juan Li and James Ramsay

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.