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The Multistage Stratified Random Sampling Method was used: \n- First Stage: Stratification based on irrigation type - major irrigation, minor irrigation, rainfed, and Mahaweli. \n- Second Stage: Random selection of 110 Farmer Organizations within 11 Divisional Secretariat Divisions (DSDs). \n- Third Stage: Random selection of 10 farming households from each selected Farmer Organization, with three reserve households to ensure the sample size is met if there are non-responses.","coll_mode":["Computer Assisted Personal Interview [capi]"],"research_instrument":"The data collection process for the Sustainable Intensification of Crop Production in Anuradhapura District involved a comprehensive Household Survey Questionnaire, meticulously crafted by the Environmental and Water Resources Management Division of HARTI in collaboration with FAO. Drawing from previous surveys, the questionnaire was specifically tailored to meet the project's objectives. 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The documents do not mention the conduct of a pilot survey or detailed anecdotal reports from the field teams.","weight":"Sample weights were calculated for the household data to ensure representativeness and account for the sampling design. The following weighting variables are used:\n- First Stage Weight (firststageweight): Computed as the inverse of the probability of selection at the village level. This weight accounts for the probability of a village being selected within each stratum (irrigation type).\n- Second Stage Weight (secondstageweights): Calculated as the inverse of the probability of selecting a Farmer Organization within each selected village. This weight adjusts for the different probabilities of selecting Farmer Organizations based on their size.\n- Third Stage Weight (thirdstageweights): Determined as the inverse of the probability of selecting a household within each Farmer Organization. This weight ensures that each household within a selected Farmer Organization is appropriately represented.\n- Final Weight (finalweights): This is the product of the first, second, and third stage weights. The final weight is normalized so that the total weighted number of households equals the total unweighted number of households. This weight is used in the analysis to ensure that survey estimates are representative of the entire population of farming households in Anuradhapura District.","cleaning_operations":"The microdata files for the Sustainable Intensification of Crop Production in Anuradhapura District were carefully processed to ensure they met several critical data quality and privacy standards. Specifically, the files were stripped of any variables that could directly identify a data subject, such as names, phone numbers, ID numbers, addresses, or geo-references. Sensitive information that could potentially cause harm, such as HIV status, was also excluded. All categorical variables were properly labeled, and missing values were clearly coded and labeled. Each dataset included a unique identifier or a combination of variables that uniquely identified every record. Numerical variables were checked to ensure they fell within realistic thresholds, and any variables with all missing values were removed. Additionally, if the dataset had a hierarchical structure, the relationships between datasets were clarified with unique identification variables to facilitate accurate data merging. If any weighting factors were required but missing, an explanation was provided along with guidance on the appropriate use of the dataset."}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). 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