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As conducting market research involves responses/information from a specific group of people, it’s often impossible to obtain the said information from every person who falls under that group. In such situations, a sample group must be selected.
A sample group is a subset of a population or a target population. Most researchers target a specific population in regard to their research topic, and the sample group must be selected from this population.
For example, if I’m conducting research on the eating habits of college students in the United States, my sample group must be selected from the American college-going population.
Depending on your type of research, a method of sampling must be chosen. A “method of sampling” is the way in which the sample group is selected from the population. There are two primary types of sampling methods, and they are as follows;
As stated earlier, probability sampling involves the choosing of a sample group through a form of “random selection”. This means that a prominent characteristic of this method is that every person in the population (or target population) is given an equal and known chance of being a part of the sample group.
To build on the previous example, if the research on the eating habits of american-college going students was to be done using probability sampling, then every american college-going student would have an equal chance of being represented in the research. Therefore, if there were 1000 college-going students in the USA, each one of them would have a 1/1000 probability of being selected to be a part of the sample group.
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There are four main kinds of probability sampling;
Simple random sampling is the most common and basic form of probability sampling. With this method each individual of the target population is assigned a number, and using automated processes the sample group is selected, allowing for an unbiased representation of the group.
For simple random sampling, automated processes such as random number generators or any other techniques that allow for completely random and unbiased selection are used.
For example, if you wanted to select 10 students from a class of 50 using this simple random sampling, you would have to assign each one of the students a number. You could then use a random number generator to choose 10 out of the 50 students.
Like in simple random sampling, systematic sampling also includes assigning numbers to everyone in the target population. However, instead of being randomly chosen with no order, individuals are chosen after deciding a specific interval. It involves the selection of every nth individual.
For example, if every 3rd candidate is chosen, candidates with numbers that are multiples of 3 will be chosen (3, 6, 9, 12, 15 and so on) as the sample group of the population.
Cluster sampling involves the grouping of the population into smaller groups or “clusters”. After these clusters are formed, researchers then select random clusters to be a part of the sample group. This eliminates large chunks of the target population randomly, allowing for an unbiased selection of clusters. This method is particularly helpful whilst conducting research on large populations (for instance a country’s whole population).
Clusters can be divided in many different ways such as by city, by school, dy district, by university etc.
For example, in the study of eating habits of american college-goers, you could use cluster sampling. By dividing the population into cities, city clusters can be eliminated until the sample is downsized to the desired size to conduct the research on.
In stratified sampling, the target population is divided into multiple subpopulations. These subpopulations, or sub-groups, are created based on important shared characteristics people within the population may share. By determining these subpopulations populations, researchers can ensure each one of these subpopulations are appropriately represented in the sample size.
For example, for research on american college students, some studies may choose to divide the college-going population into subgroups based on their majors (engineering, math, business, and so on).
PROBABILITY SAMPLING TYPES
Simple random sampling
Every participant in the target population has an equal and independent chance of being selected.
It generally yields a low error, but it may require a larger sample size to achieve precision.
In this sampling, the starting point is randomly decided, and every ‘nth’ element is selected from the population.
This type can provide a similar level of sampling error as simple random sampling with a smaller sample size if there is no underlying pattern.
The population is divided into clusters, and a random sample of clusters is selected. Then a random sample of participants within the selected cluster is chosen for the final sample.
It may introduce higher errors when clusters are heterogeneous.
Stratified random sampling
It involves dividing the population into strata based on specific characteristics and then selecting a random sample from each stratum to form the final sample.
Stratification reduces the sampling error by ensuring each stratum is adequately represented.
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There are multiple advantages of probability sampling. One of the most prominent is the lack of sampling bias and systematic error as all methods of probability sampling allow for an unbiased selection of sampling groups.
Additionally, it’s highly reliable and allows researchers to make notable and useful deductions about a population.
There are three factors that you need to monitor which can affect probability sampling. These are:
Let’s look into them.
This refers to the list of participants or elements from which you will draw the sample for your research study. A comprehensive and well-defined sampling frame can help you avoid bias and ensure that your sample represents the target population.
A larger sample size often leads to a more representative data collection hence more precise and increased statistical significance.
While the aim of probability is to reduce bias in your study, you can still encounter some biases. You must be aware of potential biases like non-response bias and employ strategic measures to mitigate them.
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There are three ways to enhance reliability in probability sampling for your research.
Sampling error is the inconsistency between the sample result and the target population value. Understanding and quantifying this can help you gauge the reliability and accuracy of the findings.
Confidence intervals tell you the range within which the true value of the population is likely to fall. It offers a measure of uncertainty, helping to draw accurate conclusions from the sample data.
You must be cautious of pitfalls like bias and undercoverage when conducting probability sampling. Proper design of the research and data analysis can help minimize these concerns.
Probability sampling is an invaluable tool in the research of all fields. It provides a solid foundation for you to generalize the findings to a larger population. By considering the different types, their usage, and the factors, you can enhance the reliability and validity of the research.