Kraemer Helena C, Measelle Jeffrey R, Ablow Jennifer C, Essex Marilyn J, Boyce W Thomas, Kupfer David J
Department of Psychiatry and Behavioral Sciences, Stanford University, 4012 Quarry Road, MC 5717, Stanford, CA 94305, USA.
Am J Psychiatry. 2003 Sep;160(9):1566-77. doi: 10.1176/appi.ajp.160.9.1566.
When there exists no single source of information (informant) to validly measure a characteristic, it is typically recommended that data from multiple informants be used. In psychiatric assessment and research, however, multiple informants often provide discordant data, which further confuse the measurement. Strategies such as arbitrarily choosing one informant or using the data from all informants separately generate further problems. This report proposes a theory to explain observed patterns of interinformant discordance and suggests a new approach to using data from multiple informants to measure characteristics of interest.
Using the example of assessment of developmental psychopathology in children, the authors propose a model in which the choice of informants is based on conceptualizing the contexts and perspectives that influence expression of the characteristic of interest and then identifying informants who represent those contexts and perspectives in such a way as to have the weaknesses of one informant canceled by the strengths of another.
Applications of this approach to several datasets indicate that when these principles are followed, a more reliable and valid consensus measure is obtained, and failure to obtain a reliable, valid measure is indicative of some deviation from the principles.
In obtaining a consensus measure, the issue is not determining how many informants are needed but choosing the right set of informants.
当不存在单一信息源（提供信息者）来有效测量某一特征时，通常建议使用来自多个提供信息者的数据。然而，在精神病学评估和研究中，多个提供信息者常常提供不一致的数据，这进一步混淆了测量结果。诸如随意选择一个提供信息者或分别使用所有提供信息者的数据等策略会引发更多问题。本报告提出一种理论来解释观察到的信息提供者之间不一致的模式，并提出一种使用多个提供信息者的数据来测量感兴趣特征的新方法。
以儿童发育性精神病理学评估为例，作者提出一个模型，其中提供信息者的选择基于对影响感兴趣特征表达的背景和观点进行概念化，然后识别代表这些背景和观点的提供信息者，以使一个提供信息者的弱点被另一个提供信息者的优势所抵消。
将这种方法应用于几个数据集表明，遵循这些原则时，可以获得更可靠、有效的共识性测量结果，而未能获得可靠、有效的测量结果则表明存在一些偏离这些原则的情况。
在获得共识性测量结果时，问题不在于确定需要多少提供信息者，而在于选择正确的一组提供信息者。
