The focus of Stage 5 is to assess and evaluate the impact of the Data Powered Positive Deviance DPPD interventions both policy and community interventions It is important to undertake this step to make sure data is being used to make interventions as effective as possible and to be able to report on results at the end of the project It is also important to note that usually there are specific monitoring and evaluation M E guidelines for each organization and the suggestions provided in this stage are for a general audience There are three main elements related to the M E of DPPD interventions Measurement What data will be collected to track the outcome measure Analysis How will the monitoring data be analyzed Dissemination How will the results of the data collection be disseminated across the different stakeholders 1 2 3 Measurement The measurement can be done using the same digital outcome measure that was used to identify the positive deviants in Stage 2 However here in Stage 5 this measure will be used to see if the intervention group experienced a significant enhancement in performance i e better vegetation lower deforestation or fewer crime reports Such as the remote sensing derived index in the Somalia Niger and Indonesia pilots the crime reports and calls in Mexico and the deforestation rates in Ecuador You should then decide whether traditional data e g surveys or focus groups is also needed to track the progress and the impact of the intervention For example alongside a survey will you also need to do a focus group discussion with the intervention population to capture the challenges unexpected dynamics and benefits the group faced in adopting the solutions You might also be interested in identifying positively deviant practices and strategies that had the largest contribution to the positive outcomes or the highest prevalence among the intervention group Once you determine how the data will be collected it is necessary to decide how often it will be collected and how long after the intervention to undertake the measurement Too soon and it will not fully have had an effect Too late and exogenous factors might be impacting the community Will you need to capture the outcomes of the intervention group continuously every six months or once a year And for how long This should depend on the intervention timeline check Tool 5 1 in the tools section for further guidance Analysis The main focus of your analysis should be measuring the change caused by scaling the positively deviant practices and strategies Other changes that are not triggered or influenced by the intervention might exist They can have an effect on the outcome measure but you should consider these as secondary in your evaluation To isolate the effect of the intervention from the non intervention related effects the changes in the outcomes of the intervention group should ideally be compared with changes in the outcomes of the control group which wasn t exposed to the intervention only in the case where a control group is ethically justifiable Alternatively if you use the same digital data as in Stage 2 you might consider looking at the same group before and after the intervention This should give you a closer estimate of the intervention caused effects or differences The most prominent ways to do this comparison are randomized controlled trials RCTs or quasi experiments In RCTs both the intervention and control group units are randomly selected from the same sample of non positive deviants within the same homogeneous group Both groups are considered similar at the beginning and only the intervention sets them apart The outcomes of the intervention group are then compared with the outcomes of the control group to check if there is a significant difference in the performance of both groups In quasi experiments the intervention and control groups are not randomly selected and the units are assigned to those groups based on their choice or on convenience Experimental vs quasi experimental design which to choose https quantifyinghealth com experimental vs quasi experimental design 109 110

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