Probability sampling in research is any method of sampling in which each member of a population has a known, discrete non-zero probability of selection into the sample. This is one of the core techniques of statistical sampling since it enables the researcher to draw statistical conclusions about a given population using the sample chosen. It can be especially helpful in surveys, experiments and big-scale studies because generalizability is important in such cases.
In this blog post, we shall look at features of probability sampling, types of many and also give illustrative examples of probability sampling. Its benefits will also be discussed and a comparison will be done between it and non-probability sampling so as to enable you to know the instances you need one or the other method.
What is Probability Sampling?
Probability sampling is the form of sampling method whereby a likelihood of each unit within a population can be identified and measured so that a unit can be selected into a sample. In this form of sampling, there is the assurance that the sample is representative of the population and this guarantees that there are possibilities of making accurate conclusions about the population using sample data in this form of sampling.
The main strength of taking a probability sample is that each sample is less likely to be biased thus sample representativeness is the best chance of taking such a sample. Such a method is helpful especially in quantitative research because it can employ statistical study upon the data that have been gathered through probability methods of sampling.
Types of Probability Sampling
The types of probability sampling are various and each has some benefits based on the research context and the population that is investigated. And now to dissect them:
1. Simple Random Sampling
One of the simplest as well as common random sampling methods involves the simple random sampling. In this, all the members of the population possess an equal likelihood of being chosen. This is usually accomplished by the process of randomly selecting names or numbers out of an exhaustive list (called a sampling frame). It is a perfect method both in the case of a homogeneous population and in the case where a random sample is required to analyze.
Advantages of Simple Random Sampling:
- It is simple to apply, more so when dealing with low populations.
- All units can be chosen with an equal probability, and this aspect is fair.
Disadvantages:
- Such may not be feasible where there are large populations because of logistical issues.
2. Stratified Sampling
Stratified sampling breaks the population down into specific subgroups- so-called strata- based on a common attribute such as level of education, age or income. After identifying the strata, a random sample will be selected in every stratum. The emphasis will be to make each subgroup proportionally represented in the final sample and will hence be more representative of the total population.
An example is that, in a study on the population of 1,000 individuals, whereby you know that 60 percent of the population are males and 40 percent are females, you would make sure that your sample proportionately reflects this percentage.
When to Use Stratified Sampling:
- When you wish to make sure that particular subgroups in the population are represented.
3. Systematic Sampling
Another common method of probability sampling is systematic sampling in which a random starting point is selected and every nth item on a list is then selected. As an example, you can choose to select a starting point at random and learn every 10th individual on the list in the event you want to take a sample of 1,000 individuals.
When compared to simple random sampling, this technique is less time consuming when dealing with a large population, however, there exists the possibility of bias in case of a pattern in the list.
When to Use Systematic Sampling:
- When you have a series of choices like a list that is ordered and you need a faster way to select it.
4. Cluster Sampling
Cluster sampling involves groupings or clusters of the population, often in geographical terms but (depending on the nature of the population) possibly on a more focused basis. Instead of picking members of the whole population, whole clusters are randomly chosen and individuals are sampled within the clusters. It is frequently applied when conducting massive studies where it will be untenable to survey each and every person within a huge population.
Stratified Sampling vs Cluster Sampling:
- Stratified sampling separates the population into portions and makes sure that there is a proportional representation, and the cluster sample randomly chooses clusters and surveys the whole population in them. Stratified sampling would be perfect in circumstances where there are differences in the population but cluster sampling is employed in cases such as geographical setting that covers a large area.
Examples of Probability Sampling
To get a feel of how the various probability sampling methods are applied in real life research situations, let us consider some examples of probability sampling.
Example 1: Simple Random Sampling
Take a case where a researcher would like to conduct a survey of students in a university to establish their study habits. Assuming that the researcher gets a list of all the students enrolled s/he can select 100 students randomly by picking them up at random in the list of all the students. This makes every student have an equal opportunity of being chosen.
Example 2: Stratified Sampling
In conducting a research on employee satisfaction, a company wishes to be assured of representation by the employees in each department. The company classifies the employees along various strata (e.g. marketing, IT and HR), and arbitrarily picks two workers across strata. This will make sure that every department is proportionately included in the sample.
Example 3: Systematic Sampling
A researcher who is researching the online shopping habits of customers would like to survey every 50th customer on a database of 10,000 online customers. The sample is made up of every 50th shopper chosen randomly in the database after choosing a starting point.
Example 4: Cluster Sampling
A researcher in a large scale public health study uses a list of 100 hospitals in the country and randomly picks out 10. The researcher next administers the survey to the entire patients in these hospitals. Such an approach is more realistic than attempting to poll patients on all hospitals.
When to Use Probability Sampling
The time to apply the probability sampling is based on the objectives of the research you are undertaking and the nature of your population. Probability sampling would be the way to go in case you wish to generalize a significant population on the basis of your sample. It can help especially in quantitative studies where the researcher is required to estimate the statistics.
The probability sampling is also necessary in the case whereby the sample size is large and the researcher must reduce the bias. Nevertheless, this approach will not always be feasible or time consuming when a thorough sampling outline is lacking, or the study is considered to be exploratory.
Advantages of Probability Sampling
- Reduces Bias: Each person has an equal probability to be chosen so there is a low chance that bias will affect your sample.
- Representativeness: The probability sampling technique aimed at obtaining a sample that represents the population and will thus generate more dependent results.
- Statistical Inferences: The probability sampling can make use of statistical inferences of a high level to analyze and draw inferences concerning the population.
Comparing Probability and Non-Probability Sampling
Probability versus non-probability sampling refers to specific kinds of benefits with regards to your research intentions. Although probability sampling is ideal for large-scaled or quantitative research needing statistics, the use of non-probability sampling (such as convenience sampling or purposive sampling) is conveniently most used during qualitative studies or when time and other resources may be limited.
The main contrast between the two is that probability sampling methods provide a common probability of every member of the population being sampled whereas non-probability sampling methods do not guarantee a representative sample and thus come with higher probabilities of bias.
Sampling Frame in Research
A list of all elements within the population out of which the sample is obtained is known as a sampling frame in research. It is a vital element of probability sampling since it establishes the collection of people who could be chosen. The sample might not be representative of the population thus compromising the validity of the study results in case the sampling frame is incomplete or biased.
Representative Sample Selection
One of the most crucial objectives of a research is a representative selection of samples. A representative sample depicts the features of the whole population and makes sure that the conclusions made on the sample can be applied to the whole population. Simple random sampling and stratified sampling are some of the probability sampling methodologies that aim at assisting the researcher in choosing the most representable sample possible.
Conclusion
The concept of probability sampling is critical to knowing how to carry out reliable and valid research. With proper application of random sampling procedures, you can make sure that your sample is representative of the entire population, and this decreases the effect of bias since the findings will have greater generalization. Identifying the adequate sampling methods is one of the most critical factors in performing surveys, experiments, or observational studies in order to obtain interesting results.
When taking into account the kinds of probability sampling and probability sampling examples, you can select the most suitable one with reference to your objectives of research. Do not forget about the probability sampling advantages, and remember the occasion and use it effectively. You must be able to seek reliable, impartial, and statistically sound research data with the help of the appropriate sampling technique
