### In order to understand sampling

In order to understand sampling, it must first be defined. According to Cooper and Schindler (2014), “The basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population” (p. 338). According to Tansey (2007), “non-probability sampling techniques involve researchers drawing samples from a larger population without requiring random selection” (p. 768). There are several different kinds of nonprobability sampling and they are outlined by Cooper and Schindler (2014). These samples include: convenience sampling, judgement sampling, quota sampling, and snowball sampling. Convenience sampling is the easiest and cheapest sampling to conduct. Due to the fact that researchers can choose whomever they want for a population, convenience sampling is the most unreliable form of sampling. Unfortunately, bias can be easily introduced into this type of research. Judgement sampling is conducted when a researcher selects a sample group that fits a certain standard. Quota sampling is conducted when the researcher uses certain relevant characteristics to describe a population. The last sampling method is snowball sampling. Snowball sampling, according to Cooper and Schindler (2014), is described as “individuals are discovered and may or may not be selected through probability methods. This group is then used to refer the researcher to others who possess similar characteristics and who, in turn, identify others” (p. 360). To conduct a reasonably reliable nonprobability sample for this research problem, I believe that quota sampling would be the best option. According to El-Masri (2017), “Quota sampling is a type of convenience sampling in which researchers make sure that certain attributes of importance to their research are proportionately represented in the sample” (p. 1). Due to this, with quota sampling, researchers can attempt to improve the sample representation. Fortunately, data is available from the past two years on attendance to the Glacier Symphony. From this data, the researcher can determine who to sample. For example, from the data available from the past two years, if 45% of the concert goers were women then the researcher should have 45% of their nonprobability sample made up of women. Quota sampling is good at improving the representation of the sample. Due to being able to know the possible attendees for each type of music, and having the data from the past two years, the researcher will be able to conduct a reasonable nonprobability sample using the quota sample. Unfortunately, with nonprobability sampling, bias cannot be completely ruled out. However, it is possible to minimize bias and quota sampling is the best option to reduce bias. It is up to the researchers to ascertain that the sample is an accurate representation of the population. Romans 2:11 states that “For God shows no partiality” (ESV). As Christians, we should be attempting to imitate God in everything that we do. For 1 John 2:6 says that “Whoever says he abides in him ought to walk in the same way in which he walked” (ESV). Intentionally introducing bias into research is dishonesty. Proverbs 11:1 tells us that “A false balance is an abomination to the Lord, but a just weight is his delight” (ESV). It is important to accurately represent the entire population and conduct the sample accordingly.