Waldtest {eRm}

Item-Specific Wald Test

Description

Performs a Wald test on item-level by splitting subjects into subgroups.

Usage

## S3 method for class 'Rm'
Waldtest(object, splitcr = "median")
## S3 method for class 'wald'
print(x,...)

Arguments

object

Object of class RM.

splitcr

Split criterion for subject raw score splitting. median uses the median as split criterion, mean performs a mean-split. Optionally splitcr can also be a dichotomous vector which assigns each person to a certain subgroup (e.g., following an external criterion). This vector can be numeric, character or a factor.

x

Object of class wald.

...

Further arguments passed to or from other methods. They are ignored in this function.

Details

Items are eliminated if they not have the same number of categories in each subgroup. To avoid this problem, for RSM and PCM it is considered to use a random or another user-defined split. If the data set contains missing values and mean or median is specified as splitcriterion, means or medians are calculated for each missing value subgroup and consequently used for raw score splitting.

Value

Returns an object of class wald containing:

coef.table

Data frame with test statistics, z- and p-values.

betapar1

Beta parameters for first subgroup

se.beta1

Standard errors for first subgroup

betapar2

Beta parameters for second subgroup

se.beta2

Standard errors for second subgroup

se.beta2

Standard errors for second subgroup

spl.gr

Names and levels for splitcr.

call

The matched call.

Author(s)

Patrick Mair, Reinhold Hatzinger

References

Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer.

Fischer, G. H., and Scheiblechner, H. (1970). Algorithmen und Programme fuer das probabilistische Testmodell von Rasch [Algorithms and programs for Rasch's probabilistic test model]. Psychologische Beitraege, 12, 23-51.

See Also

LRtest, MLoef

Examples

#Wald test for Rasch model with user-defined subject split
res <- RM(raschdat2)
splitvec <- sample(1:2,25,replace=TRUE)
Waldtest(res, splitcr = splitvec)