Sustainability Planning and Monitoring

THE MPA IN ACTIONrelevance in the classification of the differentgroups.The way in which the sampling has been donemust always be reported. Data that illustrate therepresentativeness of the participating communitiesmust also be collected and reported as part of theassessments. Only then can it be assumed thatthe information is representative of the samplinguniverse.To ensure representativeness of samples drawn,it is necessary to define the sampling universecarefully. For instance, if the sample is torepresent all communities covered in past watersupply projects, all communities that have everreceived interventions from any water supplyproject must be included in the samplinguniverse. If instead the sample is intended torepresent only those communities that receivedinterventions from project X, the sampling universewill have to exclude communities that receivedinterventions from any other project. Suchconsiderations need to be combined with ethicaland practical ones. For example, this implies thatthe final sample may include communities whereservices were finally never built, partly built, orhave broken down. Reasons have to bedocumented in each case. Secondly, whileworking with communities whose systems areincomplete or seriously out of order and wherethe communities want to revive them, the MPApractitioners should be prepared help resolvethe situation in some way, or at least link suchcommunities with resources that can help themrevive their services. The MPA was not designedto be a tool for purely academic research. Itssole use for such purposes would defeat itsfundamental objective, which is equitableempowerment of all levels of stakeholders toenhance the sustainability of their services.Larger samples improve the chance of detectingsignificant differences (that is, the likelihood thatthey are due to real, rather than accidental,differences), because there is more consistency inthe possible outcomes. It is thus always advisableto take as large a sample as possible. For largerprograms, going below a sample size of 35 to 40communities is not recommended, particularly ifstatistical analysis is desired. 134.7 Preparation for fieldworkAfter having drawn the community sample, theassessment facilitation team approaches theselected communities to gauge their interest andwillingness to participate in the assessments. Duringthese preliminary visits, the team explains thepurpose and general nature of the assessmentand its costs and possible benefits for thecommunity. Before the visits, as part of overallpreparation of the fieldwork, the team will alreadyhave determined if, and in what ways, the timeand effort of the community members whoparticipate in the assessments will be compensated.It is important to realize that, just like the time of13 As common in social research, MPA scores consist predominantly of ordinal data. Statistical analyses mostappropriate for such data are graphical presentations and non-parametric statistical tests. Non-parametricstatistics include a range of tools with varying powers of accepting or rejecting research hypotheses. Verysmall sample size limits the usefulness of some non-parametric tools. Greater applicability and choice of testsis possible when sample sizes are above 35, e.g., at a sample size greater than N=35 the normalapproximation of the binomial distribution can be used; the Spearman’s r test of association needs samplesizes of at least 35-40 to accept or reject rank-order correlations of at least 0.3 at 95 percent level ofconfidence; the Chi-square test of association requires expected frequencies to be 5 or more per cell for testresults to be interpretable, which means that any cross-tabulation of the sample beyond the basic 2X2 tableneeds sample sizes of at least 30.36