grm_cumprob

internal

Implements comprehensive test data engineering methods as described in
Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for
engineering and processing test data: Classical Test Theory (CTT) with
reliability coefficients for continuous ability assessment; Item Response
Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information
functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank
Analysis (LRA) for ordinal clustering with automatic determination of cluster
numbers; Biclustering methods including infinite relational models for
simultaneous clustering of examinees and items without predefined cluster
numbers; and Bayesian Network Models (BNM) for visualizing inter-item
dependencies. Features local dependence analysis through LRA and biclustering,
parameter estimation, dimensionality assessment, and network structure
visualization for educational, psychological, and social science research.

Koji Kosugi 

exametrika

Test Theory Analysis and Biclustering

grm_cumprob function

<dl><dt>theta</dt>
<dd>latent score of subject</dd>
<dt>a</dt>
<dd>discriminant parameter of IRF</dd>
<dt>b</dt>
<dd>difficulty parameter of IRF</dd></dl>

Arguments

cumulative probability of GRM — grm_cumprob

<dl>

<dt>theta</dt>
<dd>latent score of subject</dd>


<dt>a</dt>
<dd>discriminant parameter of IRF</dd>


<dt>b</dt>
<dd>difficulty parameter of IRF</dd>

</dl>

grm_cumprob: cumulative probability of GRM

Description

Usage

Arguments