Research Design: Quantitative and Qualitative Methods

Qualitative and Quantitative Research Methods
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Qualitative and Quantitative Research Methods
A randomized sampling approach is a technique where all samples have an equal probability of being selected. In contrast, a non-random sampling considers other factors, such as the researcher’s experience, judgment, and convenience. In the former sampling method, every element has a non-zero probability to be selected (Radhakrishnan, 2014). However, in the latter sampling approach, some elements have zero probability of getting selected. For example, a non-random sampling method might involve stopping an equal number of males and females in a busy street to coincide with gender distribution in a specific population.
During the research, sampling is important since it saves time. When dealing with a large population, contacting every person takes more time. In that light, random or non-random sampling approaches help researchers to save time by selecting sample sizes to get relevant information (Radhakrishnan, 2014). Additionally, sampling is cheaper since only a certain percentage of individuals is selected to represent the entire population. Researchers can gather detailed data from sample sizes since they get the number of people they want to have based on the information required.
In a snowball sampling, participants recommend individuals who they know during research. The primary pro of snowball sampling is that referrals make it easy to get many participants within a short period (Radhakrishnan, 2014). For example, researchers get a few individuals from whom they rely on to get others. On the contrary, the con of snowball sampling is that it has a large margin of error and sampling bias. Since people refer those they know, there is a high likelihood that they share similar traits, contributing to bias and inconclusive outcomes.
Pretesting is crucial since it helps determine if respondents will be sensitive to some questions, making them hold back or conceal important data during a survey. In addition, pilot testing is preliminary small-scale studies that investigate whether crucial components of the study are feasible or not (“Pretesting and Pilot Testing,” 2016). Finally, field testing is important since it helps researchers avoid biases, ambiguity, adjust choices or interview questions to establish authenticity, and reduce repetition.
A quantitative study would be a significant challenge for a researcher not to be involved in data collection. Specifically, a researcher should determine the data to gather since the study relies on it to make conclusions and inferences. In that light, the choice of the data collected in a quantitative study is crucial, which is why researchers must be available during surveys to gather accurate data.
On the contrary, in qualitative studies, the presence of researchers during data collection is not overly crucial. In most cases, qualitative studies use data present in institutional or government databases (Kabir, 2016). For example, if the researcher wants to obtain data about the American population demographic in 2019, the individual can easily ge...