An introduction to Rasch analysis for Psychiatric practice and research
An introduction to Rasch analysis for Psychiatric practice and research
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DOI：
10.1016/j.jpsychires.2012.09.014
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发表时间：
2013-02-01
影响因子：
4.8
通讯作者：
Tennant, Alan
中科院分区：
文献类型：
--
作者：
da Rocha, Neusa Sica;Chachamovich, Eduardo;Tennant, Alan
本文旨在介绍RASCH分析在精神病学患者报告结果的背景下的主要特征。我们概述了Rasch分析的主要特征，以抑郁症状的潜在变量为例，并使用Beck抑郁症清单进行了说明。通过将数据与Rasch模型进行拟合，我们可以确认量表的结构效度，包括不变性、局部依赖性和一维性等关键属性。我们还说明了这种方法如何告知归因于尺度的数字的含义、这些数字所代表的潜在特征的数量，以及由此产生的用于分析它们的统计操作的充分性。我们会争辩说，符合Rasch模型的数据已经成为患者报告结果的一般衡量标准，因此将促进精神病学结果工具的质量改进。在以计算机自适应测试(CAT)形式对RASCH分析得出的项目进行校准的基础上，测量技术的最新进展为减轻测试负担和/或扩大可在一次会议期间收集的信息范围开辟了进一步的机会。(C)2012爱思唯尔有限公司。保留所有权利。
This article aims to present the main characteristics of Rasch analysis in the context of patient reported outcomes in Psychiatry. We present an overview of the main features of the Rasch analysis, using as an example the latent variable of depressive symptoms, with illustrations using the Beck Depression Inventory. We will show that with fitting data to the Rasch model, we can confirm the structural validity of the scale, including key attributes such as invariance, local dependency and unidimensionality. We also illustrate how the approach can inform on the meaning of the numbers attributed to scales, the amount of the latent traits that such numbers represent, and the consequent adequacy of statistical operations used to analyse them. We would argue that fitting data to the Rasch model has become the measurement standard for patient reported outcomes in general and, as a consequence will facilitate a quality improvement of outcome instruments in psychiatry. Recent advances in measurement technologies built upon the calibration of items derived from Rasch analysis in the form of computerized adaptive tests (CAT) open up further opportunities for reducing the burden of testing, and/or expanding the range of information that can be collected during a single session. (C) 2012 Elsevier Ltd. All rights reserved.