What is the importance of sampling in research?
Generally, sampling allows researchers to obtain enough data to answer the research question(s) without having to query the entire population - saving time and money. However, sampling differs depending on whether the study is quantitative or qualitative.
Quantitative sampling is based on two elements: Power Analysis (typically using G*Power3, or similar), and random selection. Power analysis is applied to determine the minimum sample size necessary to ensure that the sample and data are statistically significant. Random sampling is required to ensure that there is no bias and that the entire population is properly represented in the sample. Note that for random sampling to work, the sample must be taken from a known population, such that every Nth person or item can be randomly selected.
Qualitative sampling required far fewer participants than quantitative as the goal is not to achieve statistical significance, but rather, data saturation, i.e., when themes begin to repeat within each subsequent participant response during the data analysis phase. Experts vary in the number of participants required for data saturation, but generally falls between 10–25. Further, random sampling is typically not required, or appropriate, for qualitative data collection, but rather convenience or purposive. Of the two, purposive appears to be preferred approach as participants are targeted, based on their particular demographics.
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