fit_enorm_mst {dexterMST}

Fit the extended nominal response model on MST data

Description

Fits an Extended NOminal Response Model (ENORM) using conditional maximum likelihood (CML) or a Gibbs sampler for Bayesian estimation; both adapted for MST data

Usage

fit_enorm_mst(
  db,
  predicate = NULL,
  fixed_parameters = NULL,
  method = c("CML", "Bayes"),
  nDraws = 1000
)

Arguments

db

an dextermst db handle

predicate

logical predicate to select data to include in the analysis, see details

fixed_parameters

data.frame with columns 'item_id', 'item_score' and 'beta'

method

If CML, the estimation method will be Conditional Maximum Likelihood. If Bayes, a Gibbs sampler will be used to produce a sample from the posterior.

nDraws

Number of Gibbs samples when estimation method is Bayes.

Details

You can use the predicate to include or omit responses from the analysis, e.g. ‘p = fit_enorm_mst(db, item_id != ’some_item' & student_birthdate > '2005-01-01')'

DexterMST will automatically correct the routing rules for the purpose of the current analysis. There are some caveats though. Predicates that lead to many different designs, e.g. a predicate like response != 'NA' (which is perfectly valid but can potentially create almost as many tests as there are students) might take very long to compute.

Predicates that remove complete modules from a test, e.g. module_nbr !=2 or module_id != 'RU4' will cause an error and should be avoided.

Value

object of type 'mst_enorm'. Can be cast to a data.frame of item parameters using function ‘coef' or used in dexter’s ability functions

References

Zwitser, R. J. and Maris, G (2015). Conditional statistical inference with multistage testing designs. Psychometrika. Vol. 80, no. 1, 65-84.

Koops, J. and Bechger, T. and Maris, G. (in press); Bayesian inference for multistage and other incomplete designs. In Research for Practical Issues and Solutions in Computerized Multistage Testing. Routledge, London.