在诊断分类模型中应用负二项分布分析计数数据。

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Applied Psychological Measurement Pub Date : 2023-01-01 Epub Date: 2022-09-06 DOI:10.1177/01466216221124604
Ren Liu, Ihnwhi Heo, Haiyan Liu, Dexin Shi, Zhehan Jiang
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引用次数: 0

摘要

诊断分类模型(DCM)已被用于根据受试者对一组潜在特质的掌握情况将其分为不同的组别。除了传统的基于项目的评分方法外,还可以根据考生完成一系列类似的小任务的情况进行评分。这些分数通常被视为计数变量。为了建立计数分数模型,本研究提出了一类新的 DCM,其核心是负二项分布。我们解释了所提出的模型框架,并通过一个操作示例演示了其使用。我们进行了模拟研究,以评估所提出模型的性能,并将其与基于泊松的 DCM 进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Negative Binomial Distribution in Diagnostic Classification Models for Analyzing Count Data.

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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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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