Read the discussion below and give response:
How Would You Conduct a Reasonably Reliable Non-probability Study?
The intent of sampling is to select a small element of people and draw conclusions from the research that can apply to the entire population at large (Cooper & Schindler, 2014). Perhaps the most common example of sampling is polling in politics, where pollsters sample a small portion of the population and attempt to draw conclusions on how much support each candidate has amongst the entire population. There are two types of procedures that researchers use to select members of a sample: probability sampling and non-probability sampling. Probability sampling is typically considered to be the most accurate at representing an entire population. The characteristics of probability sampling are that "each element of the population has a nonzero probability of being selected and the probability of each possible sample can be deduced" (Schreuder, Gregoire, & Weyer, 2001, p. 283). The foundation of probability sampling is the randomness in which the sample is drawn. Each person has an equal chance of selection. Non-probability sampling, however, involves "researchers drawing samples from a larger population without requiring random selection" (Tnasey, 2007, p. 767). The key characteristic of non-probability sampling is the subjective nature of the sampling selection. Researchers have more freedom in selection of the sampler. This can be favorable in research; however, it does not provide an accurate representation of an entire population. In the example provided in the question, I am challenged to provide a reliable non-probability sample regarding concert attendance for the upcoming year's concert schedule consisting of 200 concerts. The strength of a non-probability study depends on the specific aim of the study (Transey, 2007). In this case, I have gender specific data from attendance research gathered from concerts held by the Glacier Symphony over the past two years. With this data, the most appropriate type of non-probability sampling to utilize would be quota sampling. According to Cooper and Schindler, "the logic behind quota sampling is that certain relevant characteristics describe the dimensions of the population" (2014, p. 359). The gender data available could serve as a reasonable predictive indicator as to what ratio of male and females will attend specific concerts. I would analyze this gender specific data for each type of concert. For example, perhaps jazz concerts consist of 60% female and 40% male; whereas hard rock concerts might be 60% male and 45% female. I would break each genre up and determine a sample size reflecting the gender stats. I would use measurement questions to narrow the sample down from female or male. For example, the first question would be, "Are you a male or female?"; and the second question would be, "Which genre of music would you be most likely to attend a concert?". Using this data, I could target females and males for each music genre and use the gender percentages to build a sample. Despite quota sampling's subjectivism, it is widely used by researchers for opinions and marketing (Cooper & Schindler, 2014). As a result, I would use this data to make marketing recommendations to management to help attract more concert goers. This type of sampling is beneficial because I do not need to draw conclusions from the entire population, just conclusions from a sub-population of concert goers. Regardless of the sampling method, it is important to be thorough in your research and remain disciplined in your approach. Proverbs 12:1 states, "Whoever loves discipline loves knowledge, but he who hates reproof is stupid." Knowledge of a sampling provides to a greater understanding of a population, whether it be an entire population or a subpopulation. The value cannot be overstated in business and understanding the consumer.