Results 1 to 10 of about 341,054 (151)
Sample Size Requirements for Applying Diagnostic Classification Models [PDF]
Frontiers in Psychology, 2021 Results of a comprehensive simulation study are reported investigating the effects of sample size, test length, number of attributes and base rate of mastery on item parameter recovery and classification accuracy of four DCMs (i.e., C-RUM, DINA, DINO ...
Sedat Sen, Allan S. Cohen
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Diagnostic Classification Models for Ordinal Item Responses [PDF]
Frontiers in Psychology, 2018 The purpose of this study is to develop and evaluate two diagnostic classification models (DCMs) for scoring ordinal item data. We first applied the proposed models to an operational dataset and compared their performance to an epitome of current ...
Ren Liu, Zhehan Jiang
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A diagnostic classification version of Schizotypal Personality Questionnaire using diagnostic classification models. [PDF]
Int J Methods Psychiatr Res, 2020 AbstractObjectiveTo obtain more precise and rich information from the measurements for schizotypal personality disorder (SPD), a cutting‐edge psychometric theory called diagnostic classification models (DCMs) was first employed in the present study to develop a diagnostic classification version of the Schizotypal Personality Questionnaire (DC‐SPQ ...
Xi C, Cai Y, Peng S, Lian J, Tu D.
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A New Measurement of Internet Addiction Using Diagnostic Classification Models [PDF]
Frontiers in Psychology, 2017 To obtain accurate, valid, and rich information from the questionnaires for internet addiction, a diagnostic classification test for internet addiction (the DCT-IA) was developed using diagnostic classification models (DCMs), a cutting-edge psychometric ...
Dongbo Tu +3 more
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Applying the M2 Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies [PDF]
Frontiers in Psychology, 2018 The performance of the limited-information statistic M2 for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of M2 for specific DCMs rather than general modeling frameworks are ...
Fu Chen, Yanlou Liu, Tao Xin, Ying Cui
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Assessing Language Skills Using Diagnostic Classification Models: An Example Using a Language Instrument [PDF]
International Journal of Psychological Research, 2022 The primary purpose of the present study was to inform and illustrate, using examples, the use of Diagnostic Classification Models (DCMs) for the assessment of skills and competencies in cognition and academic achievement.
Georgios D. Sideridis +2 more
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Poisson Diagnostic Classification Models: A Framework and an Exploratory Example. [PDF]
Educ Psychol Meas, 2022 Assessments with a large amount of small, similar, or often repetitive tasks are being used in educational, neurocognitive, and psychological contexts. For example, respondents are asked to recognize numbers or letters from a large pool of those and the number of correct answers is a count variable.
Liu R, Liu H, Shi D, Jiang Z.
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On the Identifiability of Diagnostic Classification Models [PDF]
Psychometrika, 2019 This paper establishes fundamental results for statistical analysis based on diagnostic classification models (DCMs). The results are developed at a high level of generality and are applicable to essentially all diagnostic classification models. In particular, we establish identifiability results for various modeling parameters, notably item response ...
Jingchen Liu +2 more
exaly +4 more sources
Examining Parameter Invariance in a General Diagnostic Classification Model [PDF]
Frontiers in Psychology, 2020 The present study aimed at investigating invariance of a diagnostic classification model (DCM) for reading comprehension across gender. In contrast to models with continuous traits, diagnostic classification models inform mastery of a finite set of ...
Hamdollah Ravand +2 more
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Generalized Bayesian method for diagnostic classification models [PDF]
PsychometrikaThis study extends the loss function-based parameter estimation method for diagnostic classification models proposed by C. Ma, de la Torre, et al. (2023, Psychometrika) to consider prior knowledge and uncertainty of sampling. To this end, we integrate the loss function-based estimation method with the generalized Bayesian method.
Yamaguchi K, Liu Y, Xu G.
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