sirt (version 1.5-0)

automatic.recode: Automatic Method of Finding Keys in a Dataset with Raw Item Responses

Description

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.

Usage

automatic.recode(data, exclude = NULL, pstart.min = 0.6, allocate = 200, 
    maxiter = 20, progress = TRUE)

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 folowing 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

#############################################################################
# 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 50res1 <- automatic.recode( data.raw1 , exclude=c(8,9) , pstart.min=.50 )
# inspect calculated keys
res1$item.stat

#############################################################################
# EXAMPLE 2: data.timssAusTwn from TAM package
#############################################################################

library(TAM)
data(data.timssAusTwn,package="TAM")
raw.resp <- data.timssAusTwn[,1:11]
res2 <- automatic.recode( data=raw.resp )