For conducting CDM analysis within the G-DINA model framework
Wenchao Ma, The University of Minnesota, wma@umn.edu Jimmy de la Torre, The University of Hong Kong
This package (Ma & de la Torre, 2020a) provides a framework for a series of cognitively diagnostic analyses for dichotomous and polytomous responses.
Various cognitive
diagnosis models (CDMs) can be calibrated using the GDINA
function, including the G-DINA model (de la Torre, 2011), the deterministic inputs,
noisy and gate (DINA; de la Torre, 2009; Junker & Sijtsma, 2001) model,
the deterministic inputs, noisy or gate (DINO; Templin & Henson, 2006)
model, the reduced reparametrized unified model (R-RUM; Hartz, 2002),
the additive CDM (A-CDM; de la Torre, 2011), and the linear logistic
model (LLM; Maris, 1999), the multiple-strategy DINA model (de la Torre, & Douglas, 2008) and models defined
by users under the G-DINA framework using different link functions and design
matrices (de la Torre, 2011). Note that the LLM is also called
compensatory RUM and the RRUM is equivalent to the generalized NIDA model.
For ordinal and nominal responses, the sequential G-DINA model (Ma, & de la Torre, 2016) can be fitted and most of the aforementioned CDMs can be used as the processing functions (Ma, & de la Torre, 2016) at the category level. Different CDMs can be assigned to different items within a single assessment. Item parameters are estimated using the MMLE/EM algorithm. Details about the estimation algorithm can be found in Ma and de la Torre (2020). The joint attribute distribution can be modeled using an independent model, a higher-order IRT model (de la Torre, & Douglas, 2004), a loglinear model (Xu & von Davier, 2008), a saturated model or a hierarchical structures (e.g., linear, divergent). Monotonicity constraints for item/category success probabilities can also be specified.
In addition, to handle multiple strategies, generalized multiple-strategy CDMs for dichotomous response (Ma & Guo, 2019) can be fitted using GMSCDM function and
diagnostic tree model (Ma, 2019) can also be estimated using DTM function for polytomous responses. Note that these functions are experimental, and are expected to be further extended
in the future. Other diagnostic approaches include the multiple-choice model (de la Torre, 2009) and an iterative latent class analysis (ILCA; Jiang, 2019).
Various Q-matrix validation methods (de la Torre, & Chiu, 2016; de la Torre & Ma, 2016; Ma & de la Torre, 2020b; Najera, Sorrel, & Abad, 2019; see Qval),
model-data fit statistics (Chen, de la Torre, & Zhang, 2013; Hansen, Cai, Monroe, & Li, 2016; Liu, Tian, & Xin, 2016; Ma, 2020; see modelfit and itemfit),
model comparison at test and item level (de la Torre, 2011; de la Torre, & Lee, 2013;
Ma, Iaconangelo, & de la Torre, 2016; Ma & de la Torre, 2019; Sorrel, Abad, Olea, de la Torre, & Barrada, 2017; Sorrel, de la Torre, Abad, & Olea, 2017; see modelcomp),
and differential item functioning (Hou, de la Torre, & Nandakumar, 2014; Ma, Terzi, Lee, & de la Torre, 2017;
see dif) can also be conducted.
To use the graphical user interface, check startGDINA.
CDM for estimating G-DINA model and a set of other CDMs; ACTCD and NPCD for nonparametric CDMs; dina for DINA model in Bayesian framework