ShinyItemAnalysis (version 1.5.4)

obtain_nrm_def: Obtain model definition for mirt's nominal model taking in account the key of correct answers

Description

Standard mirt model with itemtype = "nominal" puts the identification constrains on the item response category slopes such as \(ak_0 = 0\) and \(ak_{(K-1)} = (K - 1)\), freely estimating the rest.

While nominal item responses are unordered by definition, it is often the case that one of the item response categories is correct and the respondents endorsing this category "naturally" possess a higher latent ability. Use this function to obtain model definition where the correct response category \(k_c\) for item \(i\) with \(K\) possible response categories translates to constrains \(ak_{k_c} = (K - 1)\) and \(ak_{k_{d1}} = 0\), with \(k_{d1}\) being the first incorrect response category (i.e. the first distractor).

Usage

obtain_nrm_def(data_with_key, ...)

Value

A data.frame with the starting values, parameter numbers, estimation constrains etc. Pass it as pars argument of mirt::mirt().

Arguments

data_with_key

The output of nominal_to_int().

...

arguments passed onto mirt::mirt(). No practical use for now.

See Also

Other BLIS/BLIRT related: BlisClass-class, coef,BlisClass-method, fit_blis(), get_orig_levels(), nominal_to_int(), print.blis_coefs()

Examples

library(mirt)

# convert nominal data to integers and the original labels with correct answers
data_with_key <- nominal_to_int(HCItest[, 1:20], HCIkey)

# build model definition for {mirt} using the returned list from above
nrm_def <- obtain_nrm_def(data_with_key)

# fit the nominal model using the obtained model definition in `pars` argument
fit <- mirt(data_with_key$Data, 1, "nominal", pars = nrm_def)