1,721,063 research outputs found
Attribute Classification Accuracy Improvement: Monotonicity Constraints on the G-DINA Model
No full textCognitive Diagnosis Models (CDMs) are restricted latent class models developed to identify students’
mastery and nonmastery of multiple attributes. A common indicator of reliability in CDM is Attribute
Classification Accuracy (ACA). In this work, we explore the consequences of assuming an inappropriate
model, and propose a new version of a general CDM, G-DINA, where a monotonic constraint is included.
A simulation study is conducted to investigate how the ACA of monotonic G-DINA compares with those
of G-DINA and other reduced CDMs. The comparison involves both calibration and validation samples.
We also introduce the use of the Likelihood Ratio (LR) test to evaluate the appropriateness of imposing
this nonlinear constraint. LR Type I error and power in this context is evaluated. For comparison
purposes, the performance of AIC and BIC is also documented. Results show that the ACA of the
monotonic G-DINA model is always better than that of the G-DINA model, and approaches that of the
generating reduced CDMs. These differences were more pronounced in the validation sample indicating
that the lack of parsimony of the G-DINA model affects the generalizability and suitability of the item
parameter estimates across samples. The results also show that the LR test can be used to determine
whether or not monotonicity can be assumed. Overall, this study finds that the appropriateness of the
constrained version of the G-DINA model can be tested empirically, and its proper use (i.e., in situations
where the true CDMs cannot be assumed) leads to improved ACA
Assessing Item-Level Fit for the Sequential G-DINA Model
No full textR function to implement the item-level fit statistics used in the paper "Assessing Item-Level Fit for the Sequential G-DINA Model"
The G-DINA Model Framework
No full textThe development of cognitive diagnosis models (CDMs) has been prolific since the turn of the century; however, they have often been developed in such a way that they lack an overall connective framework. The purpose of this chapter is to review the G-DINA framework. As a general model, it subsumes several simpler and widely-known CDMs; as a general framework, it has also served as the foundation for a variety of model extensions and new methodological developments. We will also discuss associated topics, which include model estimation, Q-matrix validation, computerized adaptive testing, and model selection as they relate to the reviewed models.link_to_subscribed_fulltex
Exploring reading comprehension skill relationships through the G-DINA model
No full text© 2016 Informa UK Limited, trading as Taylor & Francis Group. By analysing the test data of 1029 British secondary school students’ performance on 20 Programme for International Student Assessment English reading items through the generalised deterministic input, noisy ‘and’ gate (G-DINA) model, the study conducted two investigations on exploring the relationships among the five reading comprehension skills defined by six English language experts. Skill groups, skill types and conflicting relationship were discovered in the investigation about the relationship among reading comprehension skills as part of the cognitive structure of the examinees, which provided insights into the sequence of reading skill training. The investigation about the relationship among reading comprehension skills in the problem-solving process, conducted at the item level, conceptualised skill relationship patterns as strategies for solving items. The study also demonstrated that saturated G-DINA model catered to the characteristics of reading comprehension skills and could be applied to tests involving highly interactive and hierarchical skills.link_to_subscribed_fulltex
The impact of missingness to the G-DINA Model
No full textMissing responses happen when an examinee misses some items which results in an incomplete data. In this study, the impact of non-ignorable missingness to a general cognitive diagnostic model, the G-DINA model, was detected in terms of the effects on both classification rate and parameter estimation
Online_Appendix – Supplemental material for Evaluating the Fit of Sequential G-DINA Model Using Limited-Information Measures
No full textSupplemental material, Online_Appendix for Evaluating the Fit of Sequential G-DINA Model Using Limited-Information Measures by Wenchao Ma in Applied Psychological Measurement</p
Modelo G-DINA aplicado al diagnóstico de desórdenes mentales
No full textActualmente, uno de los modelos de diagnóstico cognitivo (MDC) más usados es el modelo
DINA. Sin embargo, este modelo presenta varias restricciones que hacen que en muchas
ocasiones, no sea el que mejor se ajusta a la realidad. En ese contexto, nace una generalización
del modelo DINA, denominado G-DINA (Generalized deterministic input, noisy and gate).
En el presente estudio se presentan los fundamentos y propiedades del modelo G-DINA y
su aplicación en un área en el que su uso todavía no es muy común: la psicología. Así, se
evaluaron los resultados de una muestra de pacientes de un hospital general de Lima a los
que se les aplicó el test SRQ-18 que evalúa la presencia de desórdenes mentales. Se muestra
el proceso de selección del mejor modelo para cada ítem, los resultados de los parámetros
obtenidos, los diagnósticos para los 10 primeros pacientes y una distribución de los perfiles
de estos pacientes. Finalmente se presenta un estudio de simulación que tiene por finalidad
estudiar el efecto del tamaño de muestra en la estimación de los parámetros en el contexto
de la aplicación de este estudio
Classification Accuracy Effects of Q-Matrix Validation and Sample Size in DINA and G-DINA Models
Full text linkThis article studies the extend of change in latent classes, relating to students, which were calculated using DINA and Generalized-DINA(G-DINA) Models under different distributions and sub-sample sizes which were calculated using DINA and Generalized-DINA(G-DINA) Models. Main focus of this study is the results of practical application rather than statistical structure of Cognitive Diagnostic Models (CDM). The attribute the individuals master that take the test in CDM are determined categorically. For this reason, both the fit of Q matrix with data and the effect of sample size are searched in modelling the students’ category. In the case of low model data fit and inadequate sample size, the findings of this research will be a guide in how the decisions change about which attribute a student master or not. To this end, a mathematic test consisted of 18 multiple choice questions taken by a group of 1000 examinee was employed. Analyses were carried out using 5 different Q-Matrices, for which relations between test items and attributes were determined by experts, and latent classes determined by both DINA and G-DINA models were compared. Comparisons were made with a view to accuracy of values between classes associated with examinees in different sample sizes drawn from the same population and values obtained for population. Thus, for both models, whether they lead to independent results from the samples was tested for sample sizes of 30, 50, 100, 200 and 400 and effects of Q matrix- data fit on analysis results were determined. Results of analysis showed Q-matrix – data fit had significant impact on decisions about students for both models. Keywords: Cognitive Diagnostic Models, DINA model, G-DINA model, Q Matri
Modelo G-DINA aplicado al diagnóstico de desórdenes mentales
Full text linkActualmente, uno de los modelos de diagnóstico cognitivo (MDC) más usados es el modelo
DINA. Sin embargo, este modelo presenta varias restricciones que hacen que en muchas
ocasiones, no sea el que mejor se ajusta a la realidad. En ese contexto, nace una generalización
del modelo DINA, denominado G-DINA (Generalized deterministic input, noisy and gate).
En el presente estudio se presentan los fundamentos y propiedades del modelo G-DINA y
su aplicación en un área en el que su uso todavía no es muy común: la psicología. Así, se
evaluaron los resultados de una muestra de pacientes de un hospital general de Lima a los
que se les aplicó el test SRQ-18 que evalúa la presencia de desórdenes mentales. Se muestra
el proceso de selección del mejor modelo para cada ítem, los resultados de los parámetros
obtenidos, los diagnósticos para los 10 primeros pacientes y una distribución de los perfiles
de estos pacientes. Finalmente se presenta un estudio de simulación que tiene por finalidad
estudiar el efecto del tamaño de muestra en la estimación de los parámetros en el contexto
de la aplicación de este estudio.Tesi
Cognitively Diagnostic Analysis Using the G-DINA Model in R
Full text linkCognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed
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