Bolstering superficial measurement robustness with community-based data foundations

Area of Science:

• Environmental Science
• Public Health
• Methodology

Background:

• Robustness analysis identifies consistent results across different methods, indicating reliable measurements.
• Measurement robustness is crucial for detecting environmental phenomena accurately.
• However, its application in inequitable social contexts presents unique challenges.

Purpose of the Study:

• To identify a methodological problem in measurement robustness within inequitable social contexts.
• To introduce the concept of 'sacrifice of representational adequacy for generality' (SRAG).
• To propose community-based data practices as a solution to enhance measurement representational adequacy.

Main Methods:

• The study analyzes the 'sacrifice of representational adequacy for generality' (SRAG) problem.
• Two case studies are presented to illustrate SRAG in action, focusing on pollution masking and burden.
• The effectiveness of robust community-based data practices is examined.

Main Results:

• Convergent measurements, despite appearing robust, can conceal significant environmental issues like pollution masking.
• The SRAG problem highlights a trade-off between measurement generality and accurate representation of local realities.
• Community-led data practices offer a viable strategy to improve measurement goals and strategies.

Conclusions:

• Measurement robustness alone is insufficient in inequitable social contexts due to the SRAG problem.
• Community-based data practices can correct measurement strategies, promoting representational adequacy.
• Addressing SRAG is essential for equitable environmental health research and practice.