Module 15. Data processing and analysis

Data processing

Analyses for surveys designed with stratification and clustering

Additional information for interpreting nutrition indicators

Standard analysis and checks for anthropometry data


Because this micronutrient survey manual focuses on the standard micronutrient survey design (a multi-stage cluster survey with or without stratification), this module focuses on processing and analysing data from surveys with this design. In specific situations, such as a survey in a refugee camp, where the simple random sampling (SRS) method is selected, analysis should be conducted using SRS-specific procedures. This usually means that there is no need to apply survey weights or a stratum or cluster variable when running frequency tables and generating mean estimates.

Analyses of micronutrient survey data should be conducted using software that accounts for multi-stage complex survey design with stratification. Such software includes Epi Info , SAS version 8.0 or later, SPSS with the optional SPSS Complex Samples module, Stata, Sudaan, and R.

Anthropometric indices and exclusion flags

Document explaining how to calculate anthropometric indices and which exclusion flags to use


Calculating anthropometry Z-scores igrowup SAS

SAS code to calculate Z-scores


Graph to plot anthropometric indices against reference population

Graph template that can be used to plot anthropometric indices against a reference population


Instructions: Anthropometry variable cleaning and creation

This document describes how to clean and prepare anthropometry data for analysis.


Sample weights


A Practical Guide to Adjust Micronutrient Biomarkers for Inflammation Using the BRINDA Method

The Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) inflammation adjustment method uses regression correction to adjust for the confounding effects of inflammation on select micronutrient biomarkers. This paper serves as a practical guidebook for the BRINDA inflammation adjustment approach and aids users to use the BRINDA R package and SAS to streamline their analyses.


Example data dictionary

Example data dictionary that can be used as a reference when building a survey specific dictionary


Methods and analyzers for hemoglobin measurement

Journal article: Methods and analyzers for hemoglobin measurement in clinical laboratories and field settings. Whitehead RD et al. Ann. N.Y. Acad. Sci. ISSN 0077-8923.


R code - BRINDA: Computation of BRINDA Adjusted Micronutrient Biomarkers for Inflammation

The BRINDA R package is a user-friendly all-in-one R package that uses a series of functions to implement BRINDA adjustment method.

Details and R package are located here: