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Field Articleinjecting bias into the results, especially if the 2or 3 excluded clusters were disproportionatelydifferent from the included clusters (harder toaccess, more affected by the earthquake, noaccess to humanitarian relief, etc).Once data analysis began the coordinatingteam realised that the data was potentiallybiased and had to apply some retrospectivemethods during the analysis to correct theproblem. The most important lesson to takefrom this is if an ‘alternative methodology’ isused in designing a survey, it is important toadhere to the accompanying methodologicalrequirements.Lesson 4: Do you calculate required samplesize? And is your sampling unit the householdor individual?Cluster sampling for nutrition surveys has historicallyoften been conducted using a standard30x30 approach (without calculating thesurvey/context specific required sample size)and using the WHO/Expanded Programmeon Immunisation (EPI) method for householdselection. Sampling methodology has beenmoving away from the standard approaches ofalways using the 30x30 design and use of thenext-nearest household quota sampling of eligibleindividuals. In this survey a few morerecent and highly regarded sampling techniqueswere applied.Firstly, sample size was calculated based onassumed prevalences, desired precision, andassumed design effect. Hence, the standard30x30 survey deviated into a smaller, fasterand cheaper 30x20 sample. Secondly, systematic,random sampling using household listswas applied in each cluster in order to selectthe households (and to move away from thepotentially biased EPI method of proximitysampling). In applying this method, it is necessaryto pre-select the exact householdsincluded in the data collection, therefore theprimary sampling unit becomes the household,instead of the child. This means that thechildren included in the sample are only to belocated in these 20 pre-selected households. Itis unlikely that there will be exactly one childper household and it is possible that there maybe less than exactly 20 children to be foundinclusive among all the selected households.This means that in some clusters, there may beless than 20 children (and in some clustersthere may be more than 20 children). The otherreason, in this survey, to have a quota ofhouseholds was for mortality estimates whichshould be based on households, regardless ofwhether or not there are children in the household.The survey teams, with experience fromother surveys, were accustomed to using thestandard proximity sampling approach wherechildren were the primary sampling unit andexactly 30 children were sampled per cluster.When using this ‘new’ sampling techniquesthey became nervous about clusters where 20households did not yield a minimum of 20children. Thinking that this would jeopardisethe survey results, and not understanding therationale behind this alternate methodology,the teams decided that a quota system for childrenmust be applied to each cluster that yieldedless than 20 children. Consequently, surveyteams selected additional households until the20 children quota was found, resulting in somecluster data containing more than 20 households.When the data were being cleaned andprimed for analysis, the coordinating teamrecognised that a modification of the methodologyhad been made in the field by the surveyteams. With the prescribed survey methodologythere was no way that there could be morethan 20 households per cluster, therefore thechange in sampling was immediately apparent.As a solution to the problem this imposed,the households exceeding the initial 20 householdrange were excluded from the analysisleaving the original methodology intact. Here,the lessons learnt worked two ways in that rigoroussupervision could avoid extra time duringdata cleaning, while rigorous data cleaninghelps in controlling manageable mistakesmade during data collection.ConclusionThe timely results of the survey played animportant role in detailing the effects of theearthquake to the outside world, to agenciesinvolved in the relief effort, and to donorsinterested in supporting the relief effort.Although there were problems encounteredduring this survey (as there is in every survey)coordinated interagency efforts ensured thatthe quality of the results were maintained.While high quality results are essential there isa high value in lessons learned – and shared.For further information, contact: LeahRichardson, email: Leah.Richardson@wfp.orgPeople in aidMark Wamalwa, one of the Kenyan nurses workingon the ACF TFC-Home Treatment programme (seefield article)ACF-USA, 2004M Lung'aho, CARE USA, 2006Training of Trainers. CARE IYCF-E/CARE Indonesia Breastfeeding Counseling course, WTimor, Feb-Mar 2006. Dr. Endang Widyastuti, CARE Indonesia Health Programme Leaderand Dr. Herti Herjati, Program Coordinator, IYCF on extreme left, 1st row.28