class.Lee {cacIRT} R Documentation

## Computes classification accuracy and consistency with Lee's approach.

### Description

Computes classification accuracy and consistency with Lee's approach. The probability of each possible total score conditional on ability is found with `recursive.raw`. Those probabilities are grouped according to the cut scores and used to estimate the indices. See references or code for details.

### Usage

```class.Lee(cutscore, ip, ability = NULL, rdm = NULL, quadrature = NULL, D = 1.7)
Lee.D(cutscore,  ip, quadrature, D = 1.7)
Lee.P(cutscore,  ip, theta, D = 1.7)
```

### Arguments

 `cutscore` A scalar or vector of cut scores on the True Score scale. If you have cut scores on the theta scale, you can transform them with `irf` (See example for `irf`). Should not include 0 or the max total score, as the end points are added internally. `ip` Matrix of item parameters, columns are discrimination, difficultly, guessing, respectively. For 1PL and 2PL, still give a Jx3 matrix, with `ip[,1] = 1` and `ip[,3] = 0` for the 1PL for example. `ability, theta` Ability estimates for each subject. `rdm` The response data matrix with rows as subjects and columns as items `quadrature` A list containing 1) The quadrature points and 2) Their corresponding weights `D` Scaling constant for IRT parameters, defaults to 1.7, alternatively often set to 1.

### Details

Must give only one ability, rdm, or quadrature. If ability is given, those scores are used for the P method. If rdm is given, ability is estimated with MLE (perfect response patterns given a -4 or 4) and used for the P method. If quadrature, the D method is used. `class.Lee` calls `Lee.D` or `Lee.P`.

### Value

 `Marginal` A matrix with two columns of marginal accuracy and consistency per cut score (and simultaneous if multiple cutscores are given) `Conditional` A list of two matrixes, one for conditional accuracy and one for conditional consistency. Each matrix has one row per subject (or quadrature point).

### Note

In order to score above a cut, an examinee must score at or above the cut score. Since we are working on the total score scale, be aware that if a cut score is given with a decimal (like 2.4), the examinee must have a total score at the next integer or more (so 3 or more) to score above the cut.

Quinn N. Lathrop

### References

Lee, W. (2010) Classification consistency and accuracy for complex assessments using item response theory. Journal of Educational Measurement, 47, 1–17.

### Examples

```##from rdm, item parameters denote 4 item 1PL test, cut score at x=2
##only print marginal indices

params<-matrix(c(1,1,1,1,-2,1,0,1,0,0,0,0),4,3)
rdm<-sim(params, rnorm(100))

class.Lee(2, params, rdm = rdm)\$Marginal

##or from 40 quadrature points and weights, 2 cut scores