Deciding on a sampling method remains one of the first and most important steps when conducting research. Sampling refers to the selection of people or units for inclusion in a research study and aims to create a generalization of the target population to help researchers draw insights on certain attitudes and preferences.
What is Population?
The population refers to an entire set of objects, observations, or scores that have a shared characteristic. As it remains impossible to test every person in the population, the results from the sample help generalize the examined group. Before deciding on a sampling method, researchers must consider the theoretical and study populations. The theoretical population refers to the entire population the research study wishes to examine. To find this group, researchers decide on specific attributes of participants that align with the study’s purpose such as age, gender, race, ethnicity, job title, geographic location, and income. After choosing these characteristics, researchers must find the subset of the theoretical population, known as the study population. This group is established based on their accessibility, narrowing the list of potential respondents down to those who are available to participate.
Probability sampling is a technique used by researchers to ensure each member of the population has an equal chance of selection. This sampling method is used to create an accurate sample of a diverse population while reducing the sampling bias. Probability sampling has many advantages, seeing that it is a cost-effective and straightforward process that involves no technical knowledge to successfully execute.
There are 4 Types of Probability Sampling:
1.Simple random samples
Simple random sampling involves assigning each member of the sampling frame a sequential number then using an automated program, such as a random number generator or lottery system to choose as many numbers as the sample size. With this, participants are chosen by having their number randomly selected, making for a simple and fair process. This method of sampling helps reduce bias, as samples are chosen by chance instead of researchers. Using a large sample frame is also an advantage, as it allows researchers to easily choose a sample size with no limitations to the number of participants. Despite this, having too large of a population may become problematic, as managing a sizable sampling frame remains difficult. Simple random sampling remains one of the least technical methods of sampling, helping researchers save time and money while collecting quality data.
2. Systematic sampling
Systematic sampling is a technique in which researchers create a sampling interval to accurately represent the population. An example of this method would be randomly lining up a sample of participants and choosing every 3rd person down the line. To form a sample, researchers must first decide on sample size, or how many people from the population they want to participate in the study. After determining the ideal sample size, researchers assign each member a number, while dividing the size of the population by the chosen sample size. This results in the sampling interval, indicating which people will be randomly selected for the study. For example, if the sampling interval is 8 the researcher would choose every 8th person as a sample. To choose members, researchers must pick a random member to start with, adding the interval to the random number to include more participants. Though technical, systematic sampling is an unbiased, low-risk approach to acquiring participants, providing researchers with a precise representation of the population they wish to examine.
3. Stratified random samples
Stratified random sampling involves dividing the population into specific strata/subsets that have a common characteristic. To implement this method, researchers create groups called stratum. These groupings represent an element of the population and are extremely different from one another. After researchers divide the population into stratums, they select random elements from each group to become a part of the sample. Stratified random sampling has a high level of statistical accuracy, as the extreme differences in strata result in more precise data collection. With complete control over the strata size, researchers can collect useful results from small and large sample sizes. This technique works well in situations where the population is difficult to find, the researcher wants to examine one specific stratum, or the study aims to compare multiple strata.
4. Cluster samples
To perform cluster sampling, researchers must first decide on the sample size and target audience. Once determined, sampling frames are created and members of the sample are individually selected. After this step, researchers determine distinct groups with the same average members in each set. Using random selection, researchers begin to select clusters, using two-stage and multi-stage subtypes to determine samples. Cluster sampling works well when examining geographically divided populations. The implementation of this method is also time and cost-effective, helping accurately collect data without exhausting resources.
There are 5 different types of nonprobability sampling:
Convenience sampling is a method that selects participants based on their availability. Researchers use this technique when studying large populations that are too difficult to examine as a whole, selecting easy to reach samples to participate in their study. Though this method of sampling is fast and cost-effective, the data collected cannot generalize the population as a whole and may produce biased results due to its lack of vetting.
Consecutive sampling is a technique where the researcher selects a sample of the population to study, collecting results before moving on to the next group of participants. This method allows researchers to work with multiple sample groups to make changes and adjustments during their research to avoid bias. Seeing that there are multiple sample groups, conclusive results are not dependent on one sample. This size of the sample can also vary, based on the researcher’s preference. Despite this, the samples used in this technique do not accurately represent the population.
Purposive sampling non randomly selects participants who fit within specific criteria. These samples are chosen based on the researcher’s credibility and knowledge. To do this, researchers use their judgment to accept and reject potential participants when creating their research sample. This method is time and cost-effective and may be ideal when there are limited sources of primary data. Despite this, purposive sampling may result in high levels of bias and an inability to generalize results.
Quota sampling separates the sampling frame into 2 groups based on common characteristics then selects participants non randomly from each group for inclusion. There are two different types of quota sampling, controlled and uncontrolled. Controlled sampling limits the researcher’s selection of samples, whereas uncontrolled sampling allows the researcher to choose samples without restrictions. This technique allows for a more accurate representation of a population, as specific quotas prevent over-representation. Quota sampling remains an inexpensive, convenient process that takes minimal time and produces useful results.
Snowball sampling, also referred to as network sampling, uses research participants to recruit others into the study until the sample size is met. This method works best when examining hard to reach populations, as it may be easier for participants to find and convince people of similar characteristics to become a part of the study, especially if they are hesitant to identify themselves. Through snowball sampling finds samples quickly and inexpensively, this method risks a margin of error if participants are unable to recruit enough people to meet the sample size. Bias and a lack of cooperation from participants may also prevent researchers from collecting conclusive results.