Suppr超能文献

一种稳健的扩展层次有序概率模型,用于处理异方差性感知,应用于功能限制评估。

An extended hierarchical ordered probit model robust to heteroskedastic vignette perceptions with an application to functional limitation assessment.

机构信息

Southwestern University of Finance and Economics, Chengdu, China.

出版信息

PLoS One. 2021 Mar 25;16(3):e0248805. doi: 10.1371/journal.pone.0248805. eCollection 2021.

Abstract

To improve interpersonal comparability of self-reported measures, anchoring vignettes are increasingly collected in surveys and modeled as the hierarchical ordered probit (HOPIT) model. This paper-based on the idea of psychological distance-relaxes the assumption of vignette equivalence in the HOPIT by allowing for heteroscedasticity in respondents' perceptions of vignettes. Particularly, we assume that respondents who are more similar to a vignette are more familiar with the condition described and therefore are capable of forming a more precise perception of the vignette. We show evidence in favor of this extended HOPIT through Monte Carlo simulations and an application concerning self-reported vision difficulty from the WHO Study on Global Aging and Adult Health (SAGE).

摘要

为了提高自我报告测量的人际可比性,越来越多的锚定情景被收集在调查中,并被建模为层次有序概率(HOPIT)模型。本文基于心理距离的思想,通过允许受访者对情景的看法存在异方差,放宽了 HOPIT 中情景等价的假设。具体来说,我们假设与情景越相似的受访者对所描述的情况越熟悉,因此能够更准确地感知情景。我们通过蒙特卡罗模拟和一个关于世界卫生组织全球老龄化和成人健康研究(SAGE)中自我报告视力困难的应用来证明这个扩展的 HOPIT 的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7928/7993814/715dcaa541ee/pone.0248805.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验