ILCA

This function implements an iterative latent class analysis (ILCA; Jiang, 2019) approach to estimating attributes for cognitive diagnosis.

A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) <DOI:10.18637/jss.v093.i14>, Ma and de la Torre (2019) <DOI:10.1111/emip.12262>, Ma (2019) <DOI:10.1007/978-3-030-05584-4_29> and de la Torre and Akbay (2019).

Wenchao Ma

GDINA

The Generalized DINA Model Framework

Jimmy de la Torre

Miguel Sorrel

Zhehan Jiang

ILCA function

<dl><dt>dat</dt>
<dd>A required binary item response matrix.</dd>
<dt>Q</dt>
<dd>A required binary item and attribute association matrix.</dd>
<dt>seed.num</dt>
<dd>seed number; Default = 5.</dd></dl>

Arguments

Zhehan Jiang, The University of Alabama

Author

Iterative latent-class analysis — ILCA

<dl>

<dt>dat</dt>
<dd>A required binary item response matrix.</dd>


<dt>Q</dt>
<dd>A required binary item and attribute association matrix.</dd>


<dt>seed.num</dt>
<dd>seed number; Default = 5.</dd>

</dl>

ILCA: Iterative latent-class analysis

Description

Usage

Value

Arguments

Author

References

Examples