Question

identify the sampling techniques used, and discuss potential sources of bias (if any). explain. after a wildfire, a disaster area is divided into 250 equal grids. forty 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. what type of sampling is used? a. stratified sampling is used, since the disaster area is divided into grids, and a random sample is taken from each grid. b. cluster sampling is used, since the disaster area is divided into grids, and some of those grids are selected and everyone in those grids is interviewed. c. stratified sampling is used, since the disaster area is divided into grids, and a random sample is taken from each grid. d. cluster sampling is used, since the disaster area is divided into grids, and a random sample is taken from each grid.

Explanation:

Cluster sampling divides the population into groups (clusters), here the disaster - area grids. Then, entire clusters (40 grids) are selected and all elements (occupied households in the grids) are surveyed. In stratified sampling, a sample is taken from each subgroup, but here entire grids are chosen. Potential bias could arise if the selected grids are not representative of the whole disaster - area. For example, if the selected grids are in a particular part of the area that was less affected or had a different type of housing.

Answer:

B. Cluster sampling is used, since the disaster area is divided into grids, and some of those grids are selected and everyone in those grids is interviewed.