dataSimulation: Simulation Based Estimates of Root-mean-squared-err of Theta...

In TestGardener: Optimal Analysis of Test and Rating Scale Data

Simulation Based Estimates of Root-mean-squared-err of Theta Estimates

Description

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

Usage

  dataSimulation(dataList, parList, theta.pop = seq(0, 100, len = 101), 
                 nsample = 1000)

Arguments

dataList	The list object set up by function make_dataList.
parList	The list object containing objects compuated by function Analyze.
theta.pop	A vector containing true values of theta to be estimated using simulated data.
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

theta:

Score index estimates

mu:

Expected sum score estimates

al:

Total arc length estimates

thepop:

True or population score index values

mupop:

Expected sum score population values

alpop:

Total test length population values

n:

Number of items

ntheta:

Number of theta 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.

http://testgardener.azurewebsites.net

See Also

scorePerformance

TestGardener documentation built on Jan. 16, 2023, 1:06 a.m.