select.c.prior: Select the c prior for the Three-Parameter Normal Ogive Model

In bairt: Bayesian Analysis of Item Response Theory Models

Description

Select the c (guessing parameter) prior for mcmc.3pnob, through the application of Bayes Modal Estimation Equations.

Usage

select.c.prior(nitem, m = 20, ...)

Arguments

nitem	Number of alternatives for each item.
m	It is a priori weight assigned to the prior information. m = 20 by default.
...	Further arguments.

Details

Because c (guessing parameter) is bounded by 0 and 1, a Beta(α, β) prior distribution was proposed by Swaminathan and Gifford (1986). These parameters are defined as α=mp+1 and β=m(p-1)+1, where p=1/n with n = number of alternatives for each item (Harwell & Baker, 1991, p.386)

Value

A vector length 2, this indicate the c (guessing parameter) prior for mcmc.3pnob.

Author(s)

Javier Mart<c3><ad>nez

References

Harwell, M. R, & Baker, F. B. (1991). The use of Prior Distributions in Marginalized Bayesian Item Parameter Estimation: A Didactic. Psychometrika, 15, 375-389.

See Also

mcmc.3pnob and continue.mcmc.bairt.

Examples

# data for model
data("MathTest")

# selection of the prior for 5 response options
cprior <- select.c.prior(5)

# estimate model only for the first 500 examinees of the data MathTest
model3 <- mcmc.3pnob(MathTest[1:500,], iter = 300, burning = 0,
                    c.prior = cprior)

## End(Not run)

bairt documentation built on May 1, 2019, 10:56 p.m.