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tcl (version 1.0.1)

discr_test: Testing item discriminations

Description

Computes Wald (W), likelihood ratio (LR), Rao score (RS) and gradient (GR) test of hypothesis of equal item discriminations against the alternative that at least one item discriminates differently (only for binary data).

Usage

discr_test(data)

Value

A list of class tcl of test statistics, degrees of freedom, and p-values.

test

A numeric vector of Wald (W), likelihood ratio (LR), Rao score (RS), and gradient (GR)statistics.

df

A numeric vector of corresponding degrees of freedom.

pvalue

A vector of corresponding p-values.

data

Data matrix.

call

The matched call.

Arguments

data

Data matrix.

Details

The tests are based on the following model suggested in Draxler, Kurz, Gürer, and Nolte (2024)

$$ \text{logit} \big( E(Y) \big ) = \tau + \alpha + \delta (r - 1), $$

where \(E(Y)\) ist the expected value of a binary response (of a person to an item), \(r = 1, \dots, k - 1\) is the person score, i.e., number of correct responses of that person when responding to \(k\) items, \(\tau\) is the respective person parameter and \(\alpha\) and \(\delta\) are two parameters referring to the respective item. The parameter \(\alpha\) represents a baseline, i.e., the easiness or attractiveness of the respective item in person score group \(r = 1\). The parameter \(\delta\) denotes the constant change of the attractiveness of that item between successive person score groups. Thus, the model assumes a linear effect of the person score \(r\) on the logit of the probability of a correct response.

The four test statistics are derived from a conditional likelihood function in which the \(\tau\) parameters are eliminated by conditioning on the observed person scores. The hypothesis to be tested is formally given by setting all \(\delta\) parameters equal to \(0\). The alternative assumes that at least one \(\delta\) parameter is not equal to \(0\).

References

Draxler, C., Kurz, A., Guerer, C., & Nolte, J. P. (2024). An Improved Inferential Procedure to Evaluate Item Discriminations in a Conditional Maximum Likelihood Framework. Journal of Educational and Behavioral Statistics, 49(3), 403-430.

See Also

invar_test, change_test, and LLTM_test.

Examples

Run this code
if (FALSE) {
##### Dataset PISA Mathematics data.pisaMath {sirt} #####

library(sirt)
data(data.pisaMath)
y <- data.pisaMath$data[, grep(names(data.pisaMath$data), pattern = "M" )]

res <- discr_test(data = y)
# $test
#      W     LR     RS     GR
# 72.470 73.032 76.725 73.430
#
# $df
# W LR RS  GR
# 10 10 10 10
#
# $pvalue
#       W        LR        RS         GR
# "< 0.001" "< 0.001" "< 0.001" "< 0.001"
#
# $call
# discr_test(X = y)

}

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