eRm (version 0.15-4)

MLoef: Computation of Martin-Loef's LR-Test

Description

This LR-Test is based on item subgroup splitting.

Usage

MLoef(robj, splitcr = "median")

Arguments

robj

Object of class Rm.

splitcr

Split criterion to define the item groups. "median" and "mean" split items in two groups based on their items' raw scores. splitcr can also be a vector of length k (where k denotes the number of items)

Value

• MLoef returns an object of class MLoef containing:
• LR
  LR-value
• df
  degrees of freedom of the test statistic
• p.value
  p-value of the test
• fullModel
  the overall Rasch model
• subModels
  a list containing the submodels
• Lf
  log-likelihood of the full model
• Ls
  list of the sub models' log-likelihoods
• i.groups
  a list of the item groups
• splitcr
  submitted split criterion
• split.vector
  binary allocation of items to groups
• warning
  items equalling median or mean for the respective split criteria
• call
  the matched call

Details

This function implements a generalization of the Martin-Loef test for polytomous items as proposed by Christensen, Bjorner, Kreiner & Petersen (2002), but does currently not allow for missing values. If the split criterion is "median" or "mean" and one or more items' raw scores are equal the median resp. mean, MLoef will assign those items to the lower raw score group. summary.MLoef gives detailed information about the allocation of all items. summary and print methods are available for objects of class MLoef. An 'exact' version of the Martin-Loef test for binary items is implemented in the function NPtest.

References

Christensen, K. B., Bjorner, J. B., Kreiner S. & Petersen J. H. (2002). Testing unidimensionality in polytomous Rasch models. Psychometrika, (67)4, 563--574. Fischer, G. H., and Molenaar, I. (1995). Rasch Models -- Foundations, Recent Developements, and Applications. Springer. Rost, J. (2004). Lehrbuch Testtheorie -- Testkonstruktion. Bern: Huber.

See Also

LRtest, Waldtest

Examples

# Martin-Loef-test on dichotomous Rasch model using "median" and a user-defined
# split vector. Note that group indicators can be of character and/or numeric.
splitvec <- c(1, 1, 1, "x", "x", "x", 0, 0, 1, 0)

res <- RM(raschdat1[,1:10])

MLoef.1 <- MLoef(res, splitcr = "median")
MLoef.2 <- MLoef(res, splitcr = splitvec)

MLoef.1

summary(MLoef.2)