Automatic Method of Finding Keys in a Dataset with Raw Item Responses
automatic.recode.Rd
This function calculates keys of a dataset with raw item responses. It starts with setting the most frequent category of an item to 1. Then, in each iteration keys are changed such that the highest item discrimination is found.
Arguments
- data
Dataset with raw item responses
- exclude
Vector with categories to be excluded for searching the key
- pstart.min
Minimum probability for an initial solution of keys.
- allocate
Maximum number of categories per item. This argument is used in the function
tam.ctt3
of the TAM package.- maxiter
Maximum number of iterations
- progress
A logical which indicates if iteration progress should be displayed
Value
A list with following entries
- item.stat
Data frame with item name, p value, item discrimination and the calculated key
- data.scored
Scored data frame using calculated keys in
item.stat
- categ.stats
Data frame with statistics for all categories of all items
Examples
if (FALSE) {
#############################################################################
# EXAMPLE 1: data.raw1
#############################################################################
data(data.raw1)
# recode data.raw1 and exclude keys 8 and 9 (missing codes) and
# start with initially setting all categories larger than 50
res1 <- sirt::automatic.recode( data.raw1, exclude=c(8,9), pstart.min=.50 )
# inspect calculated keys
res1$item.stat
#############################################################################
# EXAMPLE 2: data.timssAusTwn from TAM package
#############################################################################
miceadds::library_install("TAM")
data(data.timssAusTwn,package="TAM")
raw.resp <- data.timssAusTwn[,1:11]
res2 <- sirt::automatic.recode( data=raw.resp )
}