tam.ctt: Classical Test Theory Based Statistics and Plots

Description

This function computes some item statistics based on classical test theory.

Usage

tam.ctt(resp, wlescore=NULL, pvscores=NULL, group=NULL , progress=TRUE)
tam.ctt2(resp, wlescore=NULL, group=NULL , allocate=30 , progress=TRUE)
tam.ctt3(resp, wlescore=NULL, group=NULL , allocate=30 , progress=TRUE)
plotctt( resp , theta , Ncuts = NULL , ask = FALSE , col.list=NULL , package="lattice" , ... )

Arguments

resp

A data frame with unscored or scored item responses

wlescore

A vector with person parameter estimates, e.g. weighted likelihood estimates obtained from tam.wle. If wlescore=NULL is chosen in tam.ctt2, then only a frequency table of all items is produced.

pvscores

A matrix with plausible values, e.g. obtained from tam.pv

group

Vector of group identifiers if descriptive statistics shall be groupwise calculated

progress

An optional logical indicating whether computation progress should be displayed.

allocate

Average number of categories per item. This argument is just used for matrix size allocations. If an error is produced, use a sufficiently higher number.

theta

A score to be conditioned

Ncuts

Number of break points for theta

ask

A logical which asks for changing the graphic from item to item. The default is FALSE.

col.list

Optional vector of colors for plotting

package

Package used for plotting. Can be "lattice" or "graphics".

...

Further arguments to be passed.

Value

index: Index variable in this data frame
group: Group identifier
itemno: Item number
item: Item
N: Number of students responding to this item
Categ: Category label
AbsFreq: Absolute frequency of category
RelFreq: Relative frequency of category
rpb.WLE: Point biserial correlation of an item category and the WLE
M.WLE: Mean of the WLE of students in this item category
SD.WLE: Standard deviation of the WLE of students in this item category
rpb.PV: Point biserial correlation of an item category and the PV
M.PV: Mean of the PV of students in this item category
SD.PV: Standard deviation of the PV of students in this item category

Details

The functions tam.ctt2 and tam.ctt3 use Rcpp code and are slightly faster. However, only tam.ctt allows the input of wlescore and pvscores.

Examples

Run this code

## Not run: 
# #############################################################################
# # EXAMPLE 1: Multiple choice data data.mc
# #############################################################################
# 
# data(data.mc)
# # estimate Rasch model for scored data.mc data
# mod <- tam.mml( resp=data.mc$scored )
# # estimate WLE
# w1 <- tam.wle( mod )
# # estimate plausible values
# set.seed(789)
# p1 <- tam.pv( mod , ntheta=500 , normal.approx=TRUE )$pv
# 
# # CTT results for raw data
# stat1 <- tam.ctt( resp=data.mc$raw , wlescore=w1$theta , pvscores=p1[,-1] )
# stat1a <- tam.ctt2( resp=data.mc$raw , wlescore=w1$theta )  # faster
# stat1b <- tam.ctt2( resp=data.mc$raw )  # only frequencies
# stat1c <- tam.ctt3( resp=data.mc$raw , wlescore=w1$theta )  # faster
# 
# # plot empirical item response curves
# plotctt( resp=data.mc$raw , theta = w1$theta , Ncuts =5 , ask=TRUE)
# # use graphics for plot
# plotctt( resp=data.mc$raw , theta = w1$theta , Ncuts =5 , ask=TRUE , package="graphics")
# # change colors
# col.list <- c( "darkred" ,  "darkslateblue" , "springgreen4" , "darkorange" , 
#                 "hotpink4" , "navy" )
# plotctt( resp=data.mc$raw , theta = w1$theta , Ncuts =5 , ask=TRUE , 
#         package="graphics" , col.list = col.list )      
# 
# # CTT results for scored data
# stat2 <- tam.ctt( resp=data.mc$scored , wlescore=w1$theta , pvscores=p1[,-1] )
# 
# # descriptive statistics for different groups
# # define group identifier
# group <- c( rep(1,70) , rep(2,73) )
# stat3 <- tam.ctt( resp=data.mc$raw , wlescore=w1$theta , pvscores=p1[,-1] , group=group)
# stat3a <- tam.ctt2( resp=data.mc$raw , wlescore=w1$theta ,  group=group)
# ## End(Not run)