perm

perm3

perm4

<p>The data matrix of the partial orders of the attribute patterns used for the first labeling method (<code>method="1"</code>; See <code>labeling</code> for details). Available matrices are <code>perm3</code> and <code>perm4</code> for \(K=3\) and \(K=4\), respectively.</p>

datasets

perm.data

Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; <doi:10.1007/s11336-009-9125-0>). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

Wenchao Ma

ACTCD

Asymptotic Classification Theory for Cognitive Diagnosis

perm.data function

<p><code>perm3</code> is a \(48 \times 3\) matrix and <code>perm4</code> is a \(1680384 \times 3\) matrix.</p>

Format

The partial orders of the attribute patterns for labeling — perm.data

The partial orders of the attribute patterns for <code>labeling</code>

perm.data: The partial orders of the attribute patterns for `labeling`

Description

Usage

Arguments

Format