Walmart conducts a Cluster Random Sample from all 50 states of their employees. Which method should they use after selecting random clusters to avoid bias?

To ensure that Walmart avoids bias after selecting random clusters, the most appropriate method is a stratified random sample. In the context of conducting a cluster random sample, once the clusters (in this case, the states with employees) have been identified, stratification helps ensure that the sample reflects the diversity within those clusters.

Stratifying means dividing the population into distinct subgroups (strata) that share similar characteristics, such as job roles or demographics, before sampling from each stratum. This approach ensures representation from all subgroups within the clusters, which can be crucial for collecting comprehensive and unbiased data.

Using a stratified random sample after identifying clusters allows for a more nuanced understanding of employee opinions and behaviors across different segments of the workforce, enhancing the reliability of the analysis. It mitigates the risk of over-representing or under-representing any group due to the natural variations that might exist within the clusters.

This method is essential in contexts like Walmart, where employee demographics and job functionalities can vary considerably across states and locations.