Cronbach-Mesbah Curve

Description

To assess the unidimensionality of a set of items from alpha coefficient.

Usage

alpha.c(test)

Arguments

Details

To construct the curve takes the next step by step: 1. The first step uses all items to compute alpha. 2. One item is removed from the scale. The removed item is that which leaves the scale with its maximum alpha value. If we remove a bad item, the alpha coefficient will increase, whereas if we remove a good item alpha must decrease. 3. This procedure is repeated until only two items remain. This function was extracted from CMC_1.0 package.

Value

The number of items used to calculate the coefficient.

The maximum value of the alpha coefficient calculated at each step.

The item removed at each step.

Tue Cronbach-Mesbah curve plot.

References

Cameletti, M. & Caviezel, V. (2010). Checking the unidimensionality in R using the Cronbach-Mesbah curve.

Mesbah, M. (2010). Statistical quality of life. In "Method and Applications of Statistics in the Life and Health Sciences", N. BalakrishnanEd., Wiley, pp. 839-864.

Examples

data <- simulateTest(model="2PL",items=10,individuals=1000)
curve <- alpha.c(data$test)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(IRTpp)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/IRTpp/alpha.c.Rd_%03d_medium.png", width=480, height=480)
> ### Name: alpha.c
> ### Title: Cronbach-Mesbah Curve
> ### Aliases: alpha.c
> 
> ### ** Examples
> 
> data <- simulateTest(model="2PL",items=10,individuals=1000)
> curve <- alpha.c(data$test)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>