Related Experiment Video
Updated: Mar 2, 2026

Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Interobserver error in a large scale anthropometric survey
Claire C Gordon1, Bruce Bradtmiller2
1Anthropology Branch, Science & Advanced Technology Directorate, U.S. Army Natick Research, Development, and Engineering Center, Natick, Massachusetts 01760-5020.
Abstract:
The adverse effects of interobserver error on morphometric population comparisons are well documented in the literature. While interobserver error can rarely be avoided, it can be minimized by having a single individual locate and mark relevant landmarks, by limiting the number of observers for each variable, and by reviewing repeated measures data daily to catch and correct measurer drift during data collection. In this study, two pairs of experts participated in interobserver error trials designed to pre-set observer error limits for use in the quality control of a large scale anthropometric survey. Repeatability data were also collected twice daily in the field and reviewed with the measurers. Interobserver errors obtained in the field were lower than those achieved by the experts for 27 of 30 dimensions. These results suggest that establishment of permissible interobserver error in advance of data collection and frequent review of repeated measurements during data collection can reduce the magnitude of interobserver error below that obtained by experts measuring in a laboratory setting. However, even differences of small magnitude can be serios when they are directional, and 17 of 30 dimensions exhibited statistically significant bias between measurers despite all quality control efforts. The magnitudes of interobserver error observed in this study have proven particularly useful in evaluating the biological relevance of statistically significant differences which are of relatively small magnitude.
More Related Videos
Related Concept Videos
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Random and Systematic Errors
One-Way ANOVA: Unequal Sample Sizes
Bias in Epidemiological Studies
Confounding in Epidemiological Studies
One-Way ANOVA: Equal Sample Sizes
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...

