dataSimulation: Simulation Based Estimates of Error Variation of Score Index...

In TestGardener: Information Analysis for Test and Rating Scale Data

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

TestGardener documentation built on Sept. 30, 2024, 9:35 a.m.