GDINA: An R Package for Cognitive Diagnosis Modeling

Cognitive diagnosis models (CDMs) have attracted increasing attention in educational measurement because of their potential to provide diagnostic feedback about students' strengths and weaknesses. This article introduces the feature-rich R package GDINA for conducting a variety of CDM analyses. Built upon a general model framework, a number of CDMs can be calibrated using the GDINA package. Functions are also available for evaluating model-data fit, detecting differential item functioning, validating the item and attribute association, and examining classification accuracy. A grapical user interface is also provided for researchers who are less familar with R. This paper contains both technical details about model estimation and illustrations about how to use the package for data analysis. The GDINA package is also used to replicate published results, showing that it could provide comparable model parameter estimation

Bridging Parametric and Nonparametric Methods in Cognitive Diagnosis

A number of parametric and nonparametric methods for estimating cognitive diagnosis models (CDMs) have been developed and applied in a wide range of contexts. However, in the literature, a wide chasm exists between these two families of methods, and their relationship to each other is not well understood. In this paper, we propose a unified estimation framework to bridge the divide between parametric and nonparametric methods in cognitive diagnosis to better understand their relationship. We also develop iterative joint estimation algorithms and establish consistency properties within the proposed framework. Lastly, we present comprehensive simulation results to compare different methods, and provide practical recommendations on the appropriate use of the proposed framework in various CDM contexts

Cognitively diagnostic assessments and the cognitive diagnosis model framework

© 2014 Colegio Oficial de Psicologos de Madrid.This paper aims to identify the utility of and the need for cognitively diagnostic assessments (CDAs) in conjunction with cognitive diagnosis models (CDMs), and to outline various considerations involved in their development and use. We begin by contrasting the CDA/CDM framework against existing assessment frameworks, which are typically based on item response theory or classical test theory, and show that CDAs used in the CDM context can provide valuable diagnostic information that could enhance classroom instruction and learning. We then detail how the components of a CDA fit into the assessment triangle framework, as well as the evidence-centered design framework. Attribute identification and item development in the context of CDA are discussed, and examples from relevant research are provided. Details of CDMs, which are the statistical models that underpin the practical implementations of CDAs, are also discussed.Link_to_subscribed_fulltex

Application of cognitive diagnosis models to competency-based situational judgment tests

Profiling of jobs in terms of competency requirements has increasingly been applied in many organizational settings. Testing these competencies through situational judgment tests (SJTs) leads to validity problems because it is not usually clear which constructs SJTs measure. The primary purpose of this paper is to evaluate whether the application of cognitive diagnosis models (CDM) to competency-based SJTs can ascertain the underlying competencies measured by the items, and whether these competencies can be estimated precisely. Method: The generalized deterministic inputs, noisy “and” gate (G-DINA) model was applied to 26 situational judgment items measuring professional competencies based on the great eight model. These items were applied to 485 employees of a Spanish fi nancial company. The fi t of the model to the data and the convergent validity between the estimated competencies and personality dimensions were examined. Results: The G-DINA showed a good fi t to the data and the estimated competency factors, adapting and coping and interacting and presenting were positively related to emotional stability and extraversion, respectively. Conclusions: This work indicates that CDM can be a useful tool when measuring professional competencies through SJTs. CDM can clarify the competencies being measured and provide precise estimates of these competenciesMuchas organizaciones definen sus puestos de trabajo en base a las competencias profesionales que requieren. La medición de tales competencias mediante tests de juicio situacional (TJS) presenta problemas de validez, en tanto no suele estar claro los constructos que miden. El objetivo principal de este estudio es evaluar si la aplicación de los modelos de diagnóstico cognitivo (MDC) a estos tests permite clarificar y estimar de forma precisa las competencias medidas. Método: se aplicó el modelo G-DINA (generalized deterministic inputs, noisy “and” gate) a 26 ítems de juicio situacional que medían competencias profesionales fundamentadas en el modelo great eight. Se aplicó el test a 485 trabajadores de una entidad financiera española. Se examinó el ajuste del modelo a los datos, y la validez convergente entre las competencias estimadas y dimensiones de personalidad. Resultados: G-DINA mostró un buen ajuste a los datos, y los factores competenciales estimados adaptarse y aguantar, e interactuar y presentar mostraron una relación positiva con estabilidad emocional y extraversión, respectivamente. Conclusiones: este trabajo muestra que los MDC pueden ser una herramienta útil para la medición de competencias profesionales a través de TJS, aclarando las competencias que miden y obteniendo estimaciones precisas de las mismasThis research was supported in part by the UAM-IIC Chair for Psychometric Models and Application

Improving robustness in Q-Matrix validation using an iterative and dynamic procedure

In the context of cognitive diagnosis models (CDMs), a Q-matrix reflects the correspondence between attributes and items. The Q-matrix construction process is typically subjective in nature, which may lead to misspecifications. All this can negatively affect the attribute classification accuracy. In response, several methods of empirical Q-matrix validation have been developed. The general discrimination index (GDI) method has some relevant advantages such as the possibility of being applied to several CDMs. However, the estimation of the GDI relies on the estimation of the latent group sizes and success probabilities, which is made with the original (possibly misspecified) Q-matrix. This can be a problem, especially in those situations in which there is a great uncertainty about the Q-matrix specification. To address this, the present study investigates the iterative application of the GDI method, where only one item is modified at each step of the iterative procedure, and the required cutoff is updated considering the new parameter estimates. A simulation study was conducted to test the performance of the new procedure. Results showed that the performance of the GDI method improved when the application was iterative at the item level and an appropriate cutoff point was used. This was most notable when the original Q-matrix misspecification rate was high, where the proposed procedure performed better 96.5% of the times. The results are illustrated using Tatsuoka’s fraction-subtraction data set.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by Ministerio de Ciencia, Innovación y Universidades, Spain (Grant No. PSI2017-85022-P) and Cátedra de Modelos y Aplicaciones Psicométricas (Instituto de Ingeniería del Conocimiento and Universidad Autónoma de Madrid

Improving reliability estimation in cognitive diagnosis modeling

Cognitive diagnosis models (CDMs) are used in educational, clinical, or personnel selection settings to classify respondents with respect to discrete attributes, identifying strengths and needs, and thus allowing to provide tailored training/treatment. As in any assessment, an accurate reliability estimation is crucial for valid score interpretations. In this sense, most CDM reliability indices are based on the posterior probabilities of the estimated attribute profiles. These posteriors are traditionally computed using point estimates for the model parameters as approximations to their populational values. If the uncertainty around these parameters is unaccounted for, the posteriors may be overly peaked, deriving into overestimated reliabilities. This article presents a multiple imputation (MI) procedure to integrate out the model parameters in the estimation of the posterior distributions, thus correcting the reliability estimation. A simulation study was conducted to compare the MI procedure with the traditional reliability estimation. Five factors were manipulated: the attribute structure, the CDM model (DINA and G-DINA), test length, sample size, and item quality. Additionally, an illustration using the Examination for the Certificate of Proficiency in English data was analyzed. The effect of sample size was studied by sampling subsets of subjects from the complete data. In both studies, the traditional reliability estimation systematically provided overestimated reliabilities, whereas the MI procedure offered more accurate results. Accordingly, practitioners in small educational or clinical settings should be aware that the reliability estimation using model parameter point estimates may be positively biased. R codes for the MI procedure are made availableOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been funded by the Community of Madrid through the Pluriannual Agreement with the Universidad de Universidad Autónoma de Madrid in its Programa de Estímulo a la Investigación de Jóvenes Doctores (Reference SI3/ PJI/2021-00258), and by the Spanish Ministry of Science and Innovation (FPI BES-2016-077814

Puntuaciones tradicionales y estimaciones TRI en tests de elección forzosa con un modelo de dominancia

Background: Forced-choice tests (FCTs) were proposed to minimize response biases associated with Likert format items. It remains unclear whether scores based on traditional methods for scoring FCTs are appropriate for between-subjects comparisons. Recently, Hontangas et al. (2015) explored the extent to which traditional scoring of FCTs relates to the true scores and IRT estimates. The authors found certain conditions under which traditional scores (TS) can be used with FCTs when the underlying IRT model was an unfolding model. In this study, we examine to what extent the results are preserved when the underlying process becomes a dominance model. Method: The independent variables analyzed in a simulation study are: forced-choice format, number of blocks, discrimination of items, polarity of items, variability of intra-block diffi culty, range of diffi culty, and correlation between dimensions. Results: A similar pattern of results was observed for both models; however, correlations between TS and true thetas are higher and the differences between TS and IRT estimates are less discrepant when a dominance model involved. Conclusions: A dominance model produces a linear relationship between TS and true scores, and the subjects with extreme thetas are better measuredAntecedentes: los tests de elección forzosa (TEFs) fueron propuestos para reducir los sesgos de respuesta de ítems tipo Likert. Se cuestiona que los métodos de puntuación tradicional (PT) empleados permitan hacer comparaciones entre-sujetos. Recientemente, Hontangas et al. (2015) exploraron cómo las PTs obtenidas con diferentes TEFs se relacionan con sus puntuaciones verdaderas y estimaciones TRI, mostrando las condiciones para ser utilizadas cuando el modelo subyacente es un modelo de unfolding. El objetivo del trabajo actual es comprobar si el patrón de resultados se mantiene con un modelo de dominancia. Método: las variables independientes del estudio de simulación fueron: formato de elección forzosa, número de bloques, discriminación de los ítems, polaridad de los ítems, variabilidad de la dificultad intrabloque, rango de difi cultad del test y correlación entre dimensiones. Resultados: un patrón similar de resultados fue obtenido en ambos modelos, pero en el modelo de dominancia las correlaciones entre PTs y puntuaciones verdaderas son más altas y las diferencias entre PTs y estimaciones TRI se reducen. Conclusiones: un modelo de dominancia produce una relación lineal entre PTs y puntuaciones verdaderas, y los sujetos con puntuaciones extremas son medidos mejorThe research has been funded by the Ministry of Economy and Competitivity of Spain, project PSI2012-3334

Exploring the structure of digital literacy competence assessed using authentic software applications

Digital literacy competence (DL) is an important capacity for students' learning in a rapidly changing world. However, little is known about the empirical structure of DL. In this paper, we review major DL assessment frameworks and explore the dimensionality of DL from an empirical perspective using assessment data collected using authentic software applications, rather than simulated assessment environments. Secondary analysis on representative data collected from primary and secondary school students in Hong Kong using unidimensional and multidimensional item response theory reveals a general dimension of digital literacy performance and four specific, tool-dependent dimensions. These specific DL dimensions are defined by the software applications that students use and capture commonality among students' performance that is due to their familiarity with the assessment tools and contexts. The design of DL assessment is discussed in light of these findings, with particular emphasis on the influence of the nature of digital applications and environments used in assessment on the DL achievement scores measured

Validity and reliability of situational judgement test scores: A new approach based on cognitive diagnosis models

© The Author(s) 2016.Conventional methods for assessing the validity and reliability of situational judgment test (SJT) scores have proven to be inadequate. For example, factor analysis techniques typically lead to nonsensical solutions, and assumptions underlying Cronbach’s alpha coefficient are violated due to the multidimensional nature of SJTs. In the current article, we describe how cognitive diagnosis models (CDMs) provide a new approach that not only overcomes these limitations but that also offers extra advantages for scoring and better understanding SJTs. The analysis of the Q-matrix specification, model fit, and model parameter estimates provide a greater wealth of information than traditional procedures do. Our proposal is illustrated using data taken from a 23-item SJT that presents situations about student-related issues. Results show that CDMs are useful tools for scoring tests, like SJTs, in which multiple knowledge, skills, abilities, and other characteristics are required to correctly answer the items. SJT classifications were reliable and significantly related to theoretically relevant variables. We conclude that CDM might help toward the exploration of the nature of the constructs underlying SJT, one of the principal challenges in SJT research.Link_to_subscribed_fulltex