Automatic Method of Finding Keys in a Dataset with Raw Item Responses
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.
automatic.recode(data, exclude=NULL, pstart.min=0.6, allocate=200, maxiter=20, progress=TRUE)
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 |
maxiter |
Maximum number of iterations |
progress |
A logical which indicates if iteration progress should be displayed |
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 |
categ.stats |
Data frame with statistics for all categories of all items |
## Not run: ############################################################################# # 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 ) ## End(Not run)
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