Smits Niels, Cuijpers Pim, Beekman Aartjan T F, Smit Johannes H
Department of Clinical Psychology, Faculty of Psychology and Education, Vrije Universiteit, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands.
Int J Methods Psychiatr Res. 2007;16(3):150-60. doi: 10.1002/mpr.223.
This paper presents structurally incomplete designs as an approach to reduce the length of mental health tests. In structurally incomplete test designs, respondents only fill out a subset of the total item set. The scores on the unadministered items are estimated using methods for missing data. As an illustration, structurally incomplete test designs recording, respectively, two thirds, one half, one third and one quarter of the complete item set were applied to item scores on the Centre of Epidemiological Studies-Depression (CES-D) scale of the respondents in the Longitudinal Aging Study Amsterdam (LASA). The resulting unobserved item scores were estimated with the missing data method Data Augmentation. The complete and reconstructed data yielded very similar total scores and depression classifications. In contrast, the diagnostic accuracy of the incomplete designs decreased as the designs had more unobserved item scores. The discussion addresses the strengths and limitations of the application of incomplete designs in mental health research.
本文提出了结构不完整设计，作为一种缩短心理健康测试长度的方法。在结构不完整的测试设计中，受访者只需填写全部项目集的一个子集。未施测项目的分数使用缺失数据方法进行估计。作为示例，分别记录完整项目集的三分之二、二分之一、三分之一和四分之一的结构不完整测试设计，被应用于阿姆斯特丹纵向老龄化研究（LASA）中受访者的流行病学研究中心抑郁量表（CES-D）的项目分数。使用缺失数据方法“数据扩充”对未观测到的项目分数进行估计。完整数据和重构数据得出的总分和抑郁分类非常相似。相比之下，不完整设计的诊断准确性随着设计中未观测到的项目分数增多而降低。讨论部分阐述了在心理健康研究中应用不完整设计的优点和局限性。
