Data Research

Sampling Design

The sample population is made up of a variety of different individuals with inconsistent characteristics and demographics in whole. The sample size consists of 101 employees with many different variables within the data set. These variables include occupation, industry, and demographics, along with the wages earned for each individual within the data set. Because the definition of an “average worker” implies a specification to that individual only, and based on how diverse the economic conditions are across the United States and beyond, it is important to understand the information within the sample is more generalized than a target group of wage earners in a specific location, industry, or occupation would be. The sample population is to reflect a general idea of how all of the different variables come into play versus the main variable of wage earnings.

The sample design consists of a simple random sampling, which involves a random choosing of the individuals within the data set. In order to keep the idea generalized, and reflect the generalization of the wage earnings as a whole, random sampling is most likely the best form of design in reference to the entire population. The concept has many variables to consider, and therefore does not reflect a direct distinction between a certain industry, for example, and the current earnings. However, the focus is to target all variables and depict which variable seems to have the most transparent impact on the earnings of an individual. Hence, the reason for random sampling is simply because the topic is much too generalized across the population. The sample size of 101 employees does not reflect a proportionate sampling (20% of the population size). In light of that fact, it is safe to assume that this is not an appropriate sample size in order to round up a conclusion of what the larger picture may look like. The margin of error on the population size in reference to the sample size would be much,...