Diagnostic Classification Models for Ordinal Item Responses

Frontiers in Psychology

Ren Liu¹  Zhehan Jiang² 

[1] Psychological Sciences, University of California, Merced, Merced, CA, United States;University Libraries, University of Alabama, Tuscaloosa, AL, United States;

关键词: diagnostic classification model;    ordinal item responses;    partial credit model;    rating scales;    Bayesian estimation;    Markov Chain Monte Carlo (MCMC);   

DOI  :  10.3389/fpsyg.2018.02512

来源: DOAJ

【 摘 要 】

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 polytomous DCMs in which the ordered data structure is ignored. Findings suggest that the much more parsimonious models that we proposed performed similarly to the current polytomous DCMs and offered useful item-level information in addition to option-level information. We then performed a small simulation study using the applied study condition and demonstrated that the proposed models can provide unbiased parameter estimates and correctly classify individuals. In practice, the proposed models can accommodate much smaller sample sizes than current polytomous DCMs and thus prove useful in many small-scale testing scenarios.

【 授权许可】

Unknown