itemanalysis1: Classical Test Theory Item Analysis for Multiple-Choice Test...

In itemanalysis: Classical Test Theory Item Analysis

Classical Test Theory Item Analysis for Multiple-Choice Test Items

Description

Classicial Test Theory Item Analysis for Multiple-Choice Test Items

Usage

itemanalysis1(data, key, options, ngroup = ncol(data) + 1, correction = TRUE,
span.par=.3, verbose = T)

Arguments

data	a data frame with N rows and m columns, with N denoting the number of subjects and m denoting the number of items.
key	a vector of answer key with a length of m
options	a vector of response options for the test such as c("A","B","C","D")
ngroup	number of score groups to be use for plotting the item trace lines
correction	TRUE or FALSE. If it is TRUE, then an adjustment is made for point-biserial correlation.
span.par	a smoothing parameter to pass to ggplots when creating empirical ICCs
verbose	TRUE or FALSE. If it is TRUE, text output is printed.

Details

To be added later.

Value

plots	a list object storing the item trace line plots for each item
item.stat	a matrix of basic item statistics
dist.sel	a matrix of distractor selection proportion statistics
dist.disc	a matrix of corrected point-biserial statistics for distractors
dist.disc	a matrix of corrected biserial statistics for distractors

Author(s)

Cengiz Zopluoglu

See Also

itemanalysis2 for classical item analysis of polytomously scored items

Examples

  ## Not run: 
  
      data(dichotomous)
      head(dichotomous)
      str(dichotomous)
  
      # Key response vector
  
      key <- c("A","D","C","B","C","B","C","D","A","D","C","A","D","C","A",
              "B","D","B","A","C","A","A","C","B","C","B","D","A","A","A",
              "C","B","B","A","B","D","D","A","D","C","D","A","B","B","C",
              "D","B","C","C","B","D","A","C","B","A","D")
  
      # Use itemanalysis1 function to run the item analysis
  
        # In order to reduce running time for the example below,
        # I specify "data=dichotomous[,1:10]", so it only analyze the 
        # first 10 items.
        # You should specify "data=dichotomous" to analyze based on 56 items.
  
      item.analysis <- itemanalysis1(data=dichotomous[,1:10],
                             key=key,
                             options=c("A","B","C","D"),
                             ngroup=10,
                             correction=FALSE)
      
      item.analysis$item.stat
      
      item.analysis$dist.sel
      
      item.analysis$dist.disc
  
      item.analysis$plots[[1]]   # Item Trace Line for the first item
      item.analysis$plots[[2]]   # Item Trace Line for the second item
      item.analysis$plots[[3]]   # Item Trace Line for the third item
      item.analysis$plots[[4]]   # Item Trace Line for the fourth item
      item.analysis$plots[[5]]   # Item Trace Line for the fifth item
      item.analysis$plots[[6]]   # Item Trace Line for the sixth item
      item.analysis$plots[[7]]   # Item Trace Line for the seventh item
      item.analysis$plots[[8]]   # Item Trace Line for the eigth item
      item.analysis$plots[[9]]   # Item Trace Line for the ninth item
      item.analysis$plots[[10]]  # Item Trace Line for the tenth item
  
## End(Not run)

itemanalysis documentation built on June 14, 2022, 1:06 a.m.