FACTOR-ANALYSIS, CAUSAL INDICATORS AND QUALITY-OF-LIFE

Exploratory factor analysis (EFA) remains one of the standard and most widely used methods for demonstrating construct validity of new instr uments. However, the model for EFA makes assumptions which may not be applicable to all quality of life (QOL) instruments, and as a conseque nce 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 f actors or subscales. QOL instruments, however, frequently contain item s such as diseases, symptoms or treatment side effects, which are 'cau sal indicators.' These items may cause reduction in QOL for those pati ents experiencing them, but the reverse relationship need not apply: n ot all patients with a poor QOL need be experiencing the same set of s ymptoms. 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 tha t the patient probably suffers from that symptom. This is the reverse of the common EFA model, in which it is implicitly assumed that change s in QOL and any subscales 'cause' or are likely to be reflected by co rresponding changes in all their constituent items; thus the items in EFA are called 'effect indicators.' Furthermore, disease-related clust ers of symptoms, or treatment-induced side-effects, may result in diff erent 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, wh ilst prostate cancer patients receiving hormone therapy would have a v ery different symptom correlation structure. Since EFA is based upon a nalyzing the correlation matrix and assuming all items to be effect in dicators, it will extract factors representing consequences of the dis ease or treatment. These factors are likely to vary between different patient subgroups, according to the mode of treatment or the disease t ype and stage. Such factors contain little information about the relat ionship between the items and any underlying QOL constructs. Factor an alysis is largely irrelevant as a method of scale validation for those QOL instruments that contain causal indicators, and should only be us ed with items which are effect indicators.