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The Farmer Survey data collection utilises a population-based sampling approach to ensure that the sample accurately represents the larger population of farmers involved in the projects. This method calculates the required sample size based on the total population size and a predetermined significance level, both of which are essential for maintaining statistical validity and reliability in the survey results. Key elements of the sampling approach:
  1. Sample size calculation: The sample size is determined using a formula that takes into account the total population size and the desired confidence level (typically 95%) and margin of error (commonly set at 5%). This ensures that the sample is large enough to produce statistically significant and generalisable results. For example, a larger population requires a larger sample to maintain the same level of confidence and precision in the results.
  2. Population frame: To determine the appropriate sample size, the M&E local team needs a comprehensive list of all the farmers involved in the project. This list serves as the foundation for defining the population frame—the group of individuals from which the sample will be drawn. The population frame should be as complete and accurate as possible, encompassing all relevant farmers within the scope of the project. If there are multiple project locations or distinct groups within the farmer population (e.g., different farming practices, crops, or regions), the sampling approach may need to be stratified to ensure that these variations are adequately represented.
  3. Adjustments for non-response: When calculating the sample size, it’s also important to account for potential non-responses or incomplete data. Typically, a buffer (e.g., increasing the sample size by 10-20%) is included to compensate for this possibility and maintain the desired level of statistical power.
In the case that the population frame is higher than 300 and the sample size needs to be calculated, several reliable online tools are available. A recommended resource is Raosoft’s sample size calculator. In scenarios where a comprehensive farmer list is unavailable or cannot be obtained, alternative sampling strategies such as the snowballing or referral method are employed.
  • Snowballing technique: This method is useful in reaching populations that are difficult to sample when a list is not available. It starts with a small group of known respondents (seeds) who then refer to other potential respondents within their network, growing the sample size akin to a rolling snowball.
  • Referral method: Similar to snowballing, this technique relies on initial respondents to refer subsequent participants. This method is particularly valuable when targeting specific characteristics within a population that are known only to insiders (e.g., certain types of farmers or farming practices).
Tip: Don’t forget to note plans for disaggregation When analysing data, it’s crucial to disaggregate by variables such as gender or region to ensure representative results. Properly allocating the sample size across these disaggregation groups, like targeting an adequate number of farmers of each gender, is essential. To facilitate this, preferences for disaggregation should be identified early, and necessary variables must be included in the farmer list. Insufficient initial information can lead to a sample that is unsuitable for disaggregation, potentially resulting in biased outcomes if analysis proceeds under these conditions.