ctticc: Classical Test Theory Item Characteristic Curves

In MontclairML/ctticc: Item characteristic curve estimation from classical test theory indices

Classical Test Theory Item Characteristic Curves

Description

Item characteristic curves (ICC's) are visual indicators of important attributes of assessment items - most commonly difficulty and discrimination. Assessment specialists who examine ICC's usually do so from within the psychometric framework of either Item Response Theory (IRT) or Rasch modeling. This 'R' package provides an extension of this tradition into the Classical Test Theory (CTT) framework. The package has a 'psych' dependency that facilitate the estimation of CTT-generated difficulty and discrimination indices from a 'psych::alpha' object, and then plots the ICCs.

Usage

ctticc(dataframe,
items=dataframe,
plot= "together",
nrow=2,
ncol=3)

Arguments

dataframe	The 'dataframe' should contain binary responses from all items comprising the unidimensional scale.
item	The ‘items' field indicates which columns should be presented visually. Use the concatenate function if you’d like to specify non-sequential items (for example, 'c(1,4,7)') would present ICCs for data columns 1, 4, and 7. By default it is set to plot all the items comprising the dataframe.
plot	The 'plot' field has values of 'grid', 'separate', or 'together'. If using the 'grid' specification, you will also need to specify nrow and ncol.
nrow	If using the 'grid' specification, you will also need to specify number of rows.
ncol	If using the 'grid' specification, you will also need to specify number of columns.

Author(s)

Diego Figueiras ([e-mail])

John Kulas ([e-mail])

Examples

data(testdata)

#Plotting first 5 items of testdata in the same figure.
ctticc(data, item=1:5)

#Plotting items 10 to 18 of testdata on a grid
ctticc(data, item=10:18, plot="grid", nrow=3, ncol=3)

#Plotting items 10 to 18 of testdata separately
ctticc(data, item=10:18, plot="separate")

MontclairML/ctticc documentation built on April 14, 2025, 7:33 a.m.