An Illustration of Diagnostic Classification Modeling in Student Learning Outcomes Assessment

Jurich, Daniel; Bradshaw, Laine

doi:10.1080/15305058.2013.835728

Cited by 30 publications

(15 citation statements)

References 19 publications

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“…As is discussed in Templin and Bradshaw (2013), DCM models trade the precision of locating individuals on one continuum with binary classifications of multiple traits, which could lead to higher reliability estimates. Similar test length can be seen in studies such as Jurich and Bradshaw (2014) in which four or fewer items were used to measure each attribute.…”

Section: Datamentioning

confidence: 68%

A general diagnostic classification model for rating scales

Liu

Jiang

2019

Behav Res

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This study proposes and evaluates a general diagnostic classification model (DCM) for rating scales. We applied the proposed model to a dataset to compare its performance with traditional DCMs for polytomous items. We also conducted a simulation study based on the applied study condition in order to evaluate the parameter recovery of the proposed model. The findings suggest that the proposed model shows promise for (1) accommodating much smaller sample sizes by reducing a large number of parameters for estimation; (2) obtaining item category response probabilities and individual scores very similar to those from a traditional saturated model; and (3) providing general item information that is not available in traditional DCMs for polytomous items.

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Section: Datamentioning

confidence: 68%

A general diagnostic classification model for rating scales

Liu

Jiang

2019

Behav Res

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“…No single item showed misfit in more than five pairs, meaning no item exhibited substantial misfit in this model. Based on this metric, it was concluded that the model provided an acceptable representation of the data (Jurich & Bradshaw, 2014).…”

Section: Research Question 1: How Effectively Did Lcdm Estimate L2 Rementioning

confidence: 99%

Examining the L2 reading comprehension ability of adult ELLs: Developing a diagnostic test within the cognitive diagnostic assessment framework

Toprak

Çakır

2020

Language Testing

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Cognitive diagnostic assessment (CDA) has been applied to language assessment in a number of studies in which a diagnostic classification model (DCM) was retrofitted to the results of a non-diagnostic assessment. However, the need to apply CDA through utilization of an inductive rather than a retrofitted approach has been a recurrent theme in these studies. Thus, this study aimed to develop a diagnostic L2 reading comprehension test in English to investigate adult examinees’ reading performances in an EFL (English as a Foreign Language) academic setting. The test was based on a cognitive model of L2 reading comprehension and was administered to a sample of 1058 examinees across Turkey. The results were analyzed using log-linear cognitive diagnosis modeling (LCDM), which is one of the general DCM families subsuming other core DCMs. The findings of the study indicated that obtaining fine-grained diagnostic information about examinees’ performances in a given domain would be possible by coupling an adequate understanding of the construct with a CDA framework.

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“…In diagnostic models, the model reduction process is more similar to structural equation modeling than item response theory, in that it is a common practice to reduce the model by removing nonsignificant parameters after the estimation of the initial model. For example, Jurich & Bradshaw (2013) used the LCDM with a log-linear structural model to scale the Socialcultural Dimension Assessment version 6 (Halonen, Harris, Pastor, Abrahamson, & Huffman, 2005). In the estimation of the LCDM, four separate structural models were defined a priori: the fully saturated log-linear model, reduced model where three-and four-way interactions were removed (constrained to be 0), and a model with only main effects and two-way interactions that were constrained to be equal.…”

Section: Model Reduction In Dcmsmentioning

confidence: 99%

“…Initially, Jurich & Bradshaw (2013) estimated the model with a fully saturated measurement and structural model. Following this initial estimation they removed non-significant parameters from the measurement model, before using the reduced measurement model to evaluate the structural models.…”

Section: Model Reduction In Dcmsmentioning

confidence: 99%

Evaluating model estimation processes for diagnostic classification models

Thompson¹

2020

Preprint

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Diagnostic classification models (DCMs) are a class of models that define respondent ability on a set of predefined categorical latent variables. In recent years, the popularity of these models has begun to increase. As the community of researchers of practitioners of DCMs grow, it is important to examine the implementation of these models, including the process of model estimation. A key aspect of the estimation process that remains unexplored in the DCM literature is model reduction, or the removal of parameters from the model in order to create a simpler, more parsimonious model. The current study fills this gap in the literature by first applying several model reduction processes on a real data set, the Diagnosing Teachers’ Multiplicative Reasoning assessment (Bradshaw et al., 2014). Results from this analysis indicate that the selection of model reduction process can have large implications for the resulting parameter estimates and respondent classifications. A simulation study is then conducted to evaluate the relative performance of these various model reduction processes. The results of the simulation suggest that all model reduction processes are able to provide quality estimates of the item parameters and respondent masteries, if the model is able to converge. The findings also show that if the full model does not converge, then reducing the structural model provides the best opportunities for achieving a converged solution. Implications of this study and directions for future research are discussed.

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An Illustration of Diagnostic Classification Modeling in Student Learning Outcomes Assessment

Cited by 30 publications

References 19 publications

A general diagnostic classification model for rating scales

A general diagnostic classification model for rating scales

Examining the L2 reading comprehension ability of adult ELLs: Developing a diagnostic test within the cognitive diagnostic assessment framework

Evaluating model estimation processes for diagnostic classification models

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