# entropies: Entropy measures of inter-item dependency In TestGardener: Information Analysis for Test and Rating Scale Data

 entropies R Documentation

## Entropy measures of inter-item dependency

### Description

Entropy `I_1` is a scalar measure of how much information is required to predict the outcome of a choice number 1 exactly, and consequently is a measure of item effectiveness suitable for multiple choice tests and rating scales. Joint entropy `J_{1,2}` is a scalar measure of the cross-product of multinomial vectors 1 and 2. Mutual entropy `I_{1,2} = I_1 + I_2 - J_{1,2}` is a measure of the co-dependency of items 1 and 2, and thus the analogue of the negative log of a squared correlation `R^2`. this function computes all four types of entropies for two specificed items.

### Usage

``````entropies(index, m, n, chcemat, noption)
``````

### Arguments

 `index` A vector of length N containing score index values for each test taker. `m` The index of the first choice. `n` The index of the second choice. `chcemat` The data matrix containing the indices of choisen options for each test taker. `noption` A vector containing the number of options for all items.

### Value

A named list object containing objects produced from analyzing the simulations, one set for each simulation:

 `I_m: ` The entropy of item m. `I_n: ` The entropy of item n. `J_nm: ` The joint entropy of items m and n. `I_nm: ` The mutual entropy of items m and n.

### Author(s)

Juan Li and James Ramsay

### References

Ramsay, J. O., Li J. and Wiberg, M. (2020) Full information optimal scoring. Journal of Educational and Behavioral Statistics, 45, 297-315.

Ramsay, J. O., Li J. and Wiberg, M. (2020) Better rating scale scores with information-based psychometrics. Psych, 2, 347-360.

`Entropy_plot`

### Examples

``````#  Load needed objects
chcemat <- Quant_13B_problem_dataList\$chcemat
index   <- Quant_13B_problem_parmList\$index
noption <- matrix(5,24,1)
#  compute mutual entropies for all pairs of the first 6 items
Mvec    <- 1:6
Mlen    <- length(Mvec)
Hmutual <- matrix(0,Mlen,Mlen)
for (i1 in 1:Mlen) {
for (i2 in 1:i1) {
Result <- entropies(index, Mvec[i1], Mvec[i2], chcemat, noption)
Hmutual[i1,i2] = Result\$Hmutual
Hmutual[i2,i1] = Result\$Hmutual
}
}
print("Matrix of mutual entries (off-digagonal) and self-entropies (diagonal)")
print(round(Hmutual,3))
``````

TestGardener documentation built on Nov. 24, 2023, 5:08 p.m.