A comparison of approaches to reweighting anthropometric data

Abstract

The objective of this work is to identify the most effective techniques for reweighting anthropometric data such that it accurately represents a target user population. Seven methods are compared, including uniform weighting, stratification and permutations of nearest neighbour (NN) reweighting. The analysis illuminates the performance of existing and novel approaches to reweighting data specifically for approximating body size and shape (‘anthropometry’). While uniform weighting and stratified sampling are often used in this field, the present analysis indicates that lower-order NN approaches will produce more representative results. Although anthropometric data are crucial to the design of artefacts, tasks and environments, finding appropriate representative data is challenging. Designers and ergonomists are unlikely to find data that are simultaneously accessible, up-to-date, detailed and from the relevant population. The application of new statistical weights – reweighting – is one useful strategy for meeting this shortfall. This research indicates the best methods for reweighting and provides guidance for sampling strategies in future data collection efforts.

Practitioner Summary: Reweighting anthropometric data is one strategy for matching available data to a target user population. Stratified sampling is often used as the method for calculating weights, but it has been shown to produce inaccurate estimates. This research examines seven strategies and finds low-order NN approaches are the more accurate methods.

Keywords:

• Anthropometry
• design for human variability
• weighting
• reweighting
• binning
• clustering
• NHANES
• CAESAR

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Transportation Research Board.