scholarly journals Test-level and Item-level Model Fit Comparison of General vs. Specific Diagnostic Classification Models: A Case of True DCM

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahdieh Shafipoor ◽  
Hamdollah Ravand ◽  
Parviz Maftoon
Keyword(s):  
Model Fit ◽  
Diagnostic Classification ◽  
Fit Indices ◽  
Classification Models ◽  
Diagnostic Classification Models ◽  
Item Level ◽  
Model Fit Indices ◽  
Skill Mastery ◽  
Level Model ◽  
Test Level

AbstractThe current study compared the model fit indices, skill mastery probabilities, and classification accuracy of six Diagnostic Classification Models (DCMs): a general model (G-DINA) against five specific models (LLM, RRUM, ACDM, DINA, and DINO). To do so, the response data to the grammar and vocabulary sections of a General English Achievement Test, designed specifically for cognitive diagnostic purposes from scratch, was analyzed. The results of the test-level-model fit values obtained strong evidence in supporting the G-DINA and LLM models possessing the best model fit. In addition, the ACDM and RRUM were almost very identical to that of the G-DINA. The value indices of the DINO and DINA models were very close to each other but larger than those of the G-DINA and LLM. The model fit was also investigated at the item level, and the results revealed that model selection should be performed at the item level rather than the test level, and most of the specific models might perform well for the test. The findings of this study suggested that the relationships among the attributes of grammar and vocabulary are not ‘either-or’ compensatory or non-compensatory but a combination of both.

scholarly journals Improved Wald Statistics for Item-Level Model Comparison in Diagnostic Classification Models

2018 ◽  
Vol 43 (5) ◽  
pp. 402-414 ◽  
Author(s):  
Yanlou Liu ◽  
Björn Andersson ◽  
Tao Xin ◽  
Haiyan Zhang ◽  
Lingling Wang
Keyword(s):  
Model Comparison ◽  
Diagnostic Classification ◽  
Classification Models ◽  
Diagnostic Classification Models ◽  
Item Level ◽  
Level Model

scholarly journals The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models

2019 ◽  
Vol 45 (1) ◽  
pp. 5-31
Author(s):  
Matthew S. Johnson ◽  
Sandip Sinharay
Keyword(s):  
Posterior Probability ◽  
Real Data ◽  
Exact Calculation ◽  
Diagnostic Classification ◽  
Classification Models ◽  
Diagnostic Classification Models ◽  
Reliability Measure ◽  
Skill Mastery

One common score reported from diagnostic classification assessments is the vector of posterior means of the skill mastery indicators. As with any assessment, it is important to derive and report estimates of the reliability of the reported scores. After reviewing a reliability measure suggested by Templin and Bradshaw, this article suggests three new measures of reliability of the posterior means of skill mastery indicators and methods for estimating the measures when the number of items on the assessment and the number of skills being assessed render exact calculation computationally burdensome. The utility of the new measures is demonstrated using simulated and real data examples. Two of the suggested measures are recommended for future use.

scholarly journals Bayesian Psychometrics for Diagnostic Assessments: A Proof of Concept

2020 ◽  
Author(s):  
W. Jake Thompson
Keyword(s):  
Grain Size ◽  
Conceptual Framework ◽  
Bayesian Framework ◽  
Model Fit ◽  
Diagnostic Classification ◽  
Classification Models ◽  
Proof Of Concept ◽  
Diagnostic Classification Models ◽  
Specific Implementation ◽  
Bayesian Psychometrics

Diagnostic assessments measure the knowledge, skills, and understandings of students at a smaller and more actionable grain size than traditional scale-score assessments. Results of diagnostic assessments are reported as a mastery profile, indicating which knowledge, skills, and understandings the student has mastered and which ones may need more instruction. These mastery decisions are based on probabilities of mastery derived from diagnostic classification models (DCMs).This report outlines a Bayesian framework for the estimation and evaluation of DCMs. Findings illustrate the utility of the Bayesian framework for estimating and evaluating DCMs in applied settings. Specifically, the findings demonstrate how a variety of DCMs can be defined within the same conceptual framework. Additionally, using this framework, the evaluation of model fit is more straightforward and easier to interpret with intuitive graphics. Throughout, recommendations are made for specific implementation decisions for the estimation process and the assessment of model fit.

scholarly journals A study on development of Perceived Quality-Image Scale in Sports Organizations

2018 ◽  
Vol 15 (4) ◽  
pp. 2407
Author(s):  
Yeşim Bayrakdaroglu ◽  
Dursun Katkat
Keyword(s):  
Reliability Analysis ◽  
Model Fit ◽  
Measurement Tool ◽  
Fit Indices ◽  
Confirmatory Factor Analyses ◽  
Sports Organizations ◽  
Confirmatory Factor ◽  
Quality Image ◽  
Model Fit Indices ◽  
Marketing Activities

The purpose of this study is to research how marketing activities of international sports organizations are performed and to develop a scale determining the effects of image management on public. The audiences of interuniversity World Winter Olympic sheld in Erzurum in 2011 participated in the research. Explanatory and Confirmatory Factor Analysis, reliability analysis were performed over the data obtained. All model fit indices of 25-item and four-factor structure of quality-image scale perceived in sports organizations applied were found to be at good level. In line with the findings obtained from the explanatory and confirmatory factor analyses and reliability analysis, it can be uttered that the scale is a valid and reliable measurement tool that can be used in field researches.

scholarly journals Best Practices in the Use of Bifactor Models: Conceptual Grounds, Fit Indices and Complementary Indicators

2018 ◽  
Vol 18 (3) ◽  
Author(s):  
Pablo Ezequiel Flores-Kanter ◽  
Sergio Dominguez-Lara ◽  
Mario Alberto Trógolo ◽  
Leonardo Adrián Medrano
Keyword(s):  
Best Practices ◽  
Empirical Studies ◽  
Model Fit ◽  
Fit Indices ◽  
Research Article ◽  
Bifactor Models ◽  
Model Fit Indices ◽  
Personality Psychopathology ◽  
Using Data ◽  
Published Research

<p>Bifactor models have gained increasing popularity in the literature concerned with personality, psychopathology and assessment. Empirical studies using bifactor analysis generally judge the estimated model using SEM model fit indices, which may lead to erroneous interpretations and conclusions. To address this problem, several researchers have proposed multiple criteria to assess bifactor models, such as a) conceptual grounds, b) overall model fit indices, and c) specific bifactor model indicators. In this article, we provide a brief summary of these criteria. An example using data gathered from a recently published research article is also provided to show how taking into account all criteria, rather than solely SEM model fit indices, may prevent researchers from drawing wrong conclusions.</p>

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