Positive Deviance Data Methodological differences between PD and DPPD Positive deviants are identified using existing digital data sources as well as more traditional secondary data to control for potential confounds Their practices are then identified using primary data collected during the field research phase Positive deviants and their practices are identified using primary data collected through direct observation like interviews and surveys Data Powered Positive Deviance Coverage Positive deviants can be individuals communities administrative units or geographic areas that can be identified in large spatial areas and on temporal scales Positive deviants are generally individuals identified within very specific areas that are largely homogeneous e g an individual village Capabilities Requires local knowledge domain expertise and a combination of domain specific data knowledge advanced statistical analysis appreciative inquiry and ethnography skills Requires local knowledge domain expertise basic statistical analysis appreciative inquiry and ethnography skills Costs The initial cost of positive deviant identification can be low because it relies on readily available data However there can be significant additional costs associated with data access and analysis The initial cost of positive deviant identification is directly proportional to the sample size as it relies mainly on pri mary data collection Both PD and DPPD start with the same premise in every population there are individuals or communities who despite facing similar challenges and limitations achieve better results than their peers Both acknowledge the value of locally sourced solutions the agency of people and the need to value the relative advantage that a positive deviant might have over others However they somewhat differ methodologically as highlighted in Table 2 Table 2 Differences between Positive Deviance and Data Powered Positive Deviance 11 12

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