Using Diagnostic Classification Models in Psychological Rating Scales

Liu, Ren; Shi, Dexin

doi:10.20982/tqmp.16.5.p442

Cited by 4 publications

(2 citation statements)

References 40 publications

(29 reference statements)

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“…For K attributes, the combination of attribute possession groups forms 2K possible latent classes. Example applications of DCMs include obtaining student reading skill profiles (e.g., George & Robitzsch, 2021; Jang et al, 2013), obtaining student math and science skill profiles (e.g., Kabiri et al, 2017; Lee et al, 2011), classifying people with personality types (e.g., Liu & Shi, 2020; Xi et al, 2020), and diagnosing mental disorders for patients (e.g., de la Torre et al, 2018; Templin & Henson, 2006).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Applying Negative Binomial Distribution in Diagnostic Classification Models for Analyzing Count Data

Liu

Heo

Liu

et al. 2022

Applied Psychological Measurement

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Diagnostic classification models (DCMs) have been used to classify examinees into groups based on their possession status of a set of latent traits. In addition to traditional item-based scoring approaches, examinees may be scored based on their completion of a series of small and similar tasks. Those scores are usually considered as count variables. To model count scores, this study proposes a new class of DCMs that uses the negative binomial distribution at its core. We explained the proposed model framework and demonstrated its use through an operational example. Simulation studies were conducted to evaluate the performance of the proposed model and compare it with the Poisson-based DCM.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Applying Negative Binomial Distribution in Diagnostic Classification Models for Analyzing Count Data

Liu

Heo

Liu

et al. 2022

Applied Psychological Measurement

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“…Outside of the IRT framework, diagnostic classification models (DCMs; Rupp et al, 2010 ), a newer class of psychometric models, have demonstrated their potential to accurately classify examinees according to their latent traits. For example, Templin and Henson (2006) used DCMs to classify patients with pathological gambling disorder, Liu and Shi (2020) used DCMs to classify individuals with different personalities, and Ahn and Feuerstahler (2021) used DCMs to classify employees with organizational commitment. Compared to expert-suggested approaches for classifications, DCMs offered a new set of methods for researchers and practitioners to obtain data-suggested classifications.…”

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confidence: 99%

Diagnostic Classification Models for a Mixture of Ordered and Non-ordered Response Options in Rating Scales

Liu

Shi

et al. 2022

Applied Psychological Measurement

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When developing ordinal rating scales, we may include potentially unordered response options such as “Neither Agree nor Disagree,” “Neutral,” “Don’t Know,” “No Opinion,” or “Hard to Say.” To handle responses to a mixture of ordered and unordered options, Huggins-Manley et al. (2018) proposed a class of semi-ordered models under the unidimensional item response theory framework. This study extends the concept of semi-ordered models into the area of diagnostic classification models. Specifically, we propose a flexible framework of semi-ordered DCMs that accommodates most earlier DCMs and allows for analyzing the relationship between those potentially unordered responses and the measured traits. Results from an operational study and two simulation studies show that the proposed framework can incorporate both ordered and non-ordered responses into the estimation of the latent traits and thus provide useful information about both the items and the respondents.

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