Fayers P M, Hand D J
Unit for Clinical Research and Epidemiology, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Qual Life Res. 1997 Mar;6(2):139-50. doi: 10.1023/a:1026490117121.
Exploratory factor analysis (EFA) remains one of the standard and most widely used methods for demonstrating construct validity of new instruments. However, the model for EFA makes assumptions which may not be applicable to all quality of life (QOL) instruments, and as a consequence the results from EFA may be misleading. In particular, EFA assumes that the underlying construct of QOL (and any postulated subscales or 'factors') may be regarded as being reflected by the items in those factors or subscales. QOL instruments, however, frequently contain items such as diseases, symptoms or treatment side effects, which are 'causal indicators'. These items may cause reduction in QOL for those patients experiencing them, but the reverse relationship need not apply: not all patients with a poor QOL need be experiencing the same set of symptoms. Thus a high level of a symptom item may imply that a patient's QOL is likely to be poor, but a poor level of QOL need not imply that the patient probably suffers from that symptom. This is the reverse of the common EFA model, in which it is implicitly assumed that changes in QOL and any subscales 'cause' or are likely to be reflected by corresponding changes in all their constituent items; thus the items in EFA are called 'effect indicators.' Furthermore, disease-related clusters of symptoms, or treatment-induced side-effects, may result in different studies finding different sets of items being highly correlated; for example, a study involving lung cancer patients receiving surgery and chemotherapy might find one set of highly correlated symptoms, whilst prostate cancer patients receiving hormone therapy would have a very different symptom correlation structure. Since EFA is based upon analyzing the correlation matrix and assuming all items to be effect indicators, it will extract factors representing consequences of the disease or treatment. These factors are likely to vary between different patient subgroups, according to the mode of treatment or the disease type and stage. Such factors contain little information about the relationship between the items and any underlying QOL constructs. Factor analysis is largely irrelevant as a method of scale validation for those QOL instruments that contain causal indicators, and should only be used with items which are effect indicators.
探索性因素分析（EFA）仍然是证明新工具结构效度的标准且使用最广泛的方法之一。然而，EFA模型所做的假设可能并不适用于所有生活质量（QOL）工具，因此EFA的结果可能会产生误导。特别是，EFA假设QOL的潜在结构（以及任何假定的子量表或“因素”）可以被这些因素或子量表中的项目所反映。然而，QOL工具经常包含诸如疾病、症状或治疗副作用等项目，这些是“因果指标”。这些项目可能会导致经历这些项目的患者的QOL下降，但反之则不一定成立：并非所有QOL较差的患者都经历相同的一组症状。因此，症状项目的高水平可能意味着患者的QOL可能较差，但QOL水平较差并不一定意味着患者可能患有该症状。这与常见的EFA模型相反，在该模型中隐含地假设QOL和任何子量表的变化“导致”或可能由其所有组成项目的相应变化所反映；因此EFA中的项目被称为“效应指标”。此外，与疾病相关的症状群或治疗引起的副作用可能导致不同的研究发现不同的项目集高度相关；例如，一项涉及接受手术和化疗的肺癌患者的研究可能会发现一组高度相关的症状，而接受激素治疗的前列腺癌患者会有非常不同的症状相关结构。由于EFA基于分析相关矩阵并假设所有项目都是效应指标，它将提取代表疾病或治疗后果的因素。根据治疗方式或疾病类型和阶段，这些因素在不同的患者亚组之间可能会有所不同。这些因素几乎不包含关于项目与任何潜在QOL结构之间关系的信息。对于那些包含因果指标的QOL工具，因素分析作为一种量表验证方法在很大程度上是不相关的，并且只应与作为效应指标的项目一起使用。
