missQ

Introduces random misspecifications in a Q-matrix.
Only binary Q-matrix are supported so far.
Useful for simulation studies.

Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.

Pablo Nájera

cdmTools

Useful Tools for Cognitive Diagnosis Modeling

Miguel A. Sorrel

Francisco J. Abad

Rodrigo S. Kreitchmann

Kevin Santos

missQ function

<dl><dt>Q</dt>
<dd>A J items x K attributes Q-matrix (<code>matrix</code> or <code>data.frame</code>).</dd>
<dt>qjk</dt>
<dd>Number (or proportion, if lower than 1) of q-entries to modify in the Q-matrix.</dd>
<dt>retainJ</dt>
<dd>Number of items to retain (i.e., not modify) in the Q-matrix. It will retain the first <code>retainJ</code> items. It is useful for assuring the completeness of the misspecified Q-matrix if the first items conform one or more identity matrices. The default is 0.</dd>
<dt>Qid</dt>
<dd>Assure that the generated Q-matrix is generically identifiable. It includes <code>"none"</code> (for no identifiability assurance), <code>"DINA"</code>, <code>"DINO"</code>, or <code>"others"</code> (for other CDMs identifiability). The default is <code>"none"</code>.</dd>
<dt>seed</dt>
<dd>A seed for obtaining consistent results. If <code>NULL</code>, no seed is used. The default is <code>NULL</code>.</dd></dl>

Arguments

Pablo Nájera, Universidad Pontificia Comillas

Author

Introduce random misspecifications in Q-matrix — missQ

<dl>

<dt>Q</dt>
<dd>A J items x K attributes Q-matrix (<code>matrix</code> or <code>data.frame</code>).</dd>


<dt>qjk</dt>
<dd>Number (or proportion, if lower than 1) of q-entries to modify in the Q-matrix.</dd>


<dt>retainJ</dt>
<dd>Number of items to retain (i.e., not modify) in the Q-matrix. It will retain the first <code>retainJ</code> items. It is useful for assuring the completeness of the misspecified Q-matrix if the first items conform one or more identity matrices. The default is 0.</dd>


<dt>Qid</dt>
<dd>Assure that the generated Q-matrix is generically identifiable. It includes <code>"none"</code> (for no identifiability assurance), <code>"DINA"</code>, <code>"DINO"</code>, or <code>"others"</code> (for other CDMs identifiability). The default is <code>"none"</code>.</dd>


<dt>seed</dt>
<dd>A seed for obtaining consistent results. If <code>NULL</code>, no seed is used. The default is <code>NULL</code>.</dd>

</dl>

missQ: Introduce random misspecifications in Q-matrix

Description

Usage

Value

Arguments

Author

References

Examples