Standard analysis and checks for anthropometry data

Using the raw data collected in nutrition surveys, the WHO Anthro software developed by WHO can calculate weight-for-height/length, height/length-for-age, weight-for-age, head-circumference-for-age, mid-upper arm-circumference-for-age, and other anthropometric indices. The calculator also uses these data to generate the associated Z-scores based on international standards. The WHO Anthro software has been adapted for statistical software such as STATA, SAS, and R. It includes macros that can generate Z-scores for each anthropometric indicator for which data were collected and link these to the individual assessed. The WHO Anthro software and macros are available on the WHO website

Additionally, the WHO Anthro Survey Analyser, based on the “R and R Shiny package”, is a tool for analysing anthropometric data that updates Anthro methodology to provide more accurate estimates of the standard errors and confidence intervals for prevalence and mean Z-scores. It provides interactive graphics for data quality assessment and a summary report template offering key outputs (such as Z-score distribution graphics) for various grouping factors and nutrition status tables with accompanying prevalence and Z-score statistics. This software is currently available either online or offline. It also allows for estimating prevalence and mean Z-scores by area (urban, rural), education, wealth and other categories of interest that were measured in the survey.

Z-scores are considered outside of the normal range when they fall outside of a pre-specified range of standard deviations. The ranges differ based on the measurement (see below):

  • Weight-for-age z-score (WAZ) < −6 or WAZ > 5
  • Length/height-for-age z-score (LAZ/HAZ) < −6 or LAZ/HAZ > 6
  • Weight-for-length/height z-score (WLZ/WHZ) < −5 or WLZ/WHZ > 5

In these cases the value needs to be flagged and set to missing. You can find more information at the Anthropometric indices and exclusion flags online tool.

When reporting on anthropometry findings, some signs of quality (data quality checks) should be listed to identify trends in the data that could indicate inaccuracies in the measurements. Examples include:2

  • completeness/incompleteness, meaning the proportion of children where information is missing on variables by geographical region and by team, age group and sex;
  • sex ratio and age distribution;
  • number of cases and proportions of mismatches between length/height measurement position and recommended position, by age group;
  • digit preference charts, for weight and length/height, by geographical region, by team, whole number digit preference for weight, whole number digit preference for length/height;
  • implausible Z score values;
  • standard deviations; and
  • checks of normality (for example, whether the data are normally distributed).

Examples of anthropometry reporting can be found in the Template for reporting on the quality of anthropometric measurements online tool. Sample tables of how to report anthropometric findings are presented in the Graph to plot anthropometric indices against reference population online tool.

  1. Macros are available at UNICEF Stata Macro is available upon request via email to Note SAS and SPSS macros do not calculate confidence intervals for estimates to consider complex sample designs. An update isupdate is under development at the time of this publication. 

  2. Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF); 2019. Licence: CC BY-NC-SA 3.0 IGO,, accessed 19 June 2020).