Obtaining Diagnostic Classification Model Estimates Using Mplus

2013 ◽  
Vol 32 (2) ◽  
pp. 37-50 ◽  
Author(s):  
Jonathan Templin ◽  
Lesa Hoffman
Keyword(s):  
Classification Model ◽  
Diagnostic Classification ◽  
Diagnostic Classification Model

scholarly journals Integrating a Statistical Topic Model and a Diagnostic Classification Model for Analyzing Items in a Mixed Format Assessment

2021 ◽  
Vol 11 ◽  
Author(s):  
H.-J. Choi ◽  
Seohyun Kim ◽  
Allan S. Cohen ◽  
Jonathan Templin ◽  
Yasemin Copur-Gencturk
Keyword(s):  
Language Arts ◽  
Reading Skills ◽  
Topic Model ◽  
Classification Model ◽  
Diagnostic Classification ◽  
Constructed Response ◽  
Diagnostic Classification Model ◽  
Psychometric Models ◽  
Latent Topics ◽  
Mixed Format

Selected response items and constructed response (CR) items are often found in the same test. Conventional psychometric models for these two types of items typically focus on using the scores for correctness of the responses. Recent research suggests, however, that more information may be available from the CR items than just scores for correctness. In this study, we describe an approach in which a statistical topic model along with a diagnostic classification model (DCM) was applied to a mixed item format formative test of English and Language Arts. The DCM was used to estimate students’ mastery status of reading skills. These mastery statuses were then included in a topic model as covariates to predict students’ use of each of the latent topics in their written answers to a CR item. This approach enabled investigation of the effects of mastery status of reading skills on writing patterns. Results indicated that one of the skills, Integration of Knowledge and Ideas, helped detect and explain students’ writing patterns with respect to students’ use of individual topics.

scholarly journals A Longitudinal Higher-Order Diagnostic Classification Model

2019 ◽  
Vol 44 (3) ◽  
pp. 251-281 ◽  
Author(s):  
Peida Zhan ◽  
Hong Jiao ◽  
Dandan Liao ◽  
Feiming Li
Keyword(s):  
Parameter Estimation ◽  
Simulation Study ◽  
Higher Order ◽  
Latent Structure ◽  
Classification Model ◽  
Individual Growth ◽  
Diagnostic Classification ◽  
Modeling Approach ◽  
Local Item Dependence ◽  
Diagnostic Classification Model

Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth by constructing a multidimensional higher-order latent structure to take into account the correlations among multiple latent attributes that are examined across different occasions. In addition, potential local item dependence among anchor (or repeated) items can be taken into account. Model parameter estimation is explored in a simulation study. An empirical example is analyzed to illustrate the applications and advantages of the proposed modeling approach.

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