"Identify the sampling technique used, and discuss potential sources of bias (if any). Explain.
After a hurricane, a disaster area is divided into 200 equal grids. Thirty of the grids are selected, and every occupied household in the grid is interviewed to help focus relief efforts on what residents require the most."

Verified step by step guidance

1. Step 1: Identify the sampling technique used. This scenario describes a method where the disaster area is divided into 200 equal grids, and 30 grids are selected for interviews. This is an example of cluster sampling, where the population is divided into clusters (grids), and a subset of clusters is chosen for data collection.
2. Step 2: Explain the cluster sampling technique. In cluster sampling, the population is divided into groups (clusters) that are often naturally occurring, such as geographic areas. A random selection of clusters is made, and data is collected from all members within the selected clusters.
3. Step 3: Discuss potential sources of bias. One potential source of bias in this method is that the selected grids may not be representative of the entire disaster area. For example, some grids might have more severe damage or different socioeconomic conditions than others, leading to biased results.
4. Step 4: Consider how bias could affect the results. If the selected grids disproportionately represent certain types of households or areas, the relief efforts might be misaligned with the actual needs of the entire population. This could result in over- or under-allocation of resources to specific areas.
5. Step 5: Suggest ways to minimize bias. To reduce bias, ensure that the selection of grids is truly random and consider stratifying the grids based on key characteristics (e.g., level of damage or population density) before sampling. This can help ensure a more representative sample of the disaster area.

Key Concepts

Sampling Technique

The sampling technique refers to the method used to select individuals or units from a population for a study. In this scenario, the technique used is cluster sampling, where the population is divided into clusters (in this case, grids), and entire clusters are randomly selected for data collection. This method is efficient for large populations and can reduce costs and time.

Bias in Sampling

Bias in sampling occurs when certain members of a population are systematically more likely to be selected than others, leading to an unrepresentative sample. In the given scenario, potential sources of bias could arise if the selected grids do not reflect the diversity of the entire disaster area, or if only certain types of households are more likely to be occupied, thus skewing the results.

Data Collection

Data collection is the systematic approach to gathering information for analysis. In this context, interviewing every occupied household within the selected grids allows for comprehensive data on residents' needs. However, the method of data collection must be carefully designed to ensure that it captures accurate and relevant information, minimizing the risk of bias and enhancing the reliability of the findings.