Factor analysis, causal indicators and quality of life

Area of Science:

• Psychometrics
• Health Outcomes Research
• Statistical Modeling

Background:

• Exploratory factor analysis (EFA) is a standard method for validating new instruments, including those measuring quality of life (QOL).
• EFA relies on the assumption that items are 'effect indicators,' meaning they are reflections of an underlying construct.
• However, many QOL instruments include 'causal indicators' (e.g., symptoms, side effects) that directly impact QOL.

Purpose of the Study:

• To critically evaluate the applicability of EFA to QOL instruments containing causal indicators.
• To highlight the potential for misleading results when EFA assumptions are violated.
• To propose appropriate methods for validating QOL instruments with causal indicators.

Main Methods:

• Conceptual analysis of EFA assumptions in the context of QOL measurement.
• Distinction between 'effect indicators' and 'causal indicators' in psychometric modeling.
• Examination of how causal indicators can lead to divergent factor structures across different patient populations.

Main Results:

• EFA is often inappropriate for QOL instruments with causal indicators, as the assumed direction of causality is reversed.
• Causal indicators can lead to factors that represent consequences of disease or treatment rather than underlying QOL constructs.
• Factor structures derived from EFA may vary significantly between patient subgroups, limiting generalizability.

Conclusions:

• EFA is largely irrelevant for validating QOL instruments that predominantly use causal indicators.
• Scale validation for QOL instruments with causal indicators requires methods that account for the direction of causality.
• EFA should be reserved for QOL instruments composed solely of effect indicators.