Sampling in Research
The article 'Sampling in research' ascertains all the facets related to ‘sampling’ to decipher its relevance and importance in research.
The article 'Sampling in research' ascertains all the facets related to ‘sampling’ to decipher its relevance and importance in research. There are several types of sampling based on their segregation into separate categories, which we shall read in this article as given below. The methods and steps of sampling shall also be discussed at length.
What is Sampling?
Sampling is the process of choosing the group from which you will collect data for your research. Those individuals who make up a sample are taken from a larger population. With the help of sampling, you can test a statistical hypothesis on the traits of a population. In statistical analysis, sampling is the procedure by which researchers select a specific number of observations from a larger population. The sampling strategy will depend on the sort of study being done, although it may involve systematic sampling or just plain random sampling.
For instance: You could interview a sample of 100 students if you were examining the opinions of students at your university.
Advantages/Merits of Sampling
Everybody who has ever worked on a research project is aware that there are only a finite number of resources available at any given time. Because of this, the majority of research studies focus on gathering information from a sample of people rather than the full population (the census being one of the few exceptions). This is so that researchers can use sampling due to the following advantages:
Collection of Data that is appropriate for research
Sometimes the purpose of research is to gather a small amount of data from a large number of people (e.g., an opinion poll). Sometimes the objective is to gather a lot of data from a small number of individuals (e.g., a user study or ethnographic interview). In either case, sampling enables researchers to interview subjects in greater depth and collect richer data than contacting the entire population.
Saving of Time
It takes time to reach out to everyone in a population. Also, some persons will invariably not reply to the first attempt at contacting them, necessitating more time for researchers to follow up. A non-random sample is almost always obtained more quickly than random sampling, while random sampling is substantially slower than surveying the entire population. Hence, sampling helps researchers save a tonne of time.
Saving Money and Resources
The cost of the study is closely correlated with how many persons a researcher contacts. By enabling researchers to obtain the same results from samples as they would from the population, sampling helps researchers save money. Non-random sampling is much less expensive than random sampling since it incurs less expense in locating and obtaining participants for the data collection. Saving money is crucial because all research is done on a tight budget.
Methods/ Techniques of Sampling
Sampling can be divided into two major types, post which we can further segregate different types of sampling under it. The 2 major categories are:
a. Probability Sampling
b. Non-Probability Sampling
Let us now study different types of sampling methods under these 2 heads:
The probability sampling technique makes use of a random selection technique. In this strategy, every eligible person has a chance to choose a sample from the entire sample space. This approach takes longer and costs more money than the non-probability sampling approach. The advantage of probability sampling is that it ensures the sample will accurately reflect the population.
Various types of probability sampling are as follows:
1. Simple Random Sampling
Every item in the population has an equal and likely probability of being chosen for the sample when using a simple random sampling procedure. This method is referred to as the "Method of Chance Selection" since the decision to select an item is solely based on luck. It is referred to as "Representative Sampling" because the sample size is substantial and the item was selected at random.
2. Systematic Sampling
In this case, the population set is used to create the cluster or group of people. Similar significant traits apply to the group. Also, they have a comparable chance of being included in the sample. Simple random sampling is used in this method to sample the population cluster.
3. Stratified Sampling
Here, the sampling procedure is completed by dividing the entire population into smaller groups. The small group is made up of people who share a few traits with the general population. The statisticians choose the sample at random after dividing the population into smaller groups.
4. Clustered Sampling
Here, the target population is used to pick the items, and after a predetermined sampling interval, the other approaches are used. By dividing the total population by the required population, it is calculated.
In contrast to random selection, the researcher chooses the sample in the non-probability sampling method based on their assessment. With this methodology, not every person in the population has the opportunity to take part in the research.
1. Snowball Sampling
Chain-referral sampling technique is another name for the snowball sampling method. Samples in this method have characteristics that are challenging to find. Each member of the population who has been identified is then asked to locate the additional sampling units. These sampling units are a part of the same target group.
2. Quota Sampling
In the quota sampling method, the researcher creates a sample of people who reflect the population based on particular characteristics or attributes. The researcher selects sample subsets that produce a valuable data set that generalises to the complete population.
3. Convenience Sampling
With a convenience sampling strategy, the samples are chosen directly from the population since the researcher can easily access them. The samples are simple to choose, and the researcher avoided selecting the sample that best represents the population as a whole.
Sample Size and its Determination
The process of deciding how many observations or replicates to include in a statistical sample is known as sample size determination. Any empirical study to draw conclusions about a population from a sample must take into account the sample size as a crucial component.
Based on factors such as age, gender, and geographic area, researchers select their sample. Samples may be general or detailed. For instance, you might be interested in learning what consumers between the ages of 18 and 25 think of your product. Instead, you could just condition that your sample be American, giving you a representative sample of the entire population. The sample size is the total number of people included in a sample.
It takes more than merely distributing your survey to as many people as you can to establish how large a sample size should be. Large sample size could result in resource, time, and money wastage. A too-small sample size prevents you from getting the most information and produces conclusions that aren't conclusive.
Steps in Sample Design
The 5 steps of sampling are as follows:
1. Identification of Target Population
The term "target population" describes the set of people or things that researchers are interested in generalising their findings to. A clearly defined population decreases the likelihood of undesired people or things. A sample of the target population is taken.
2. Sampling Frame selection
The set of people or things from which the researcher will select a sample is known as the sampling frame. The actual list of every unit in the target population is what is used to create the sample.
3. Sampling Technique to be used
There are two methods for sampling: probability (random selection) and non-probability (non-random) methods. Random selection may be used for samples if the sampling frame and the target population are the same.
4. Sample Size
The quantity of units in the sample is referred to as the sample size. Determining the sample size depends on several variables, including time, cost, and facility.
5. Execution of the Sampling Plan
The researcher can use all of this knowledge to carry out the sampling plan and gather the data needed for the research once the population, sampling frame, sampling technique, and sample size have been determined.
Principles/Essentials of Sampling
The following are the top three sampling tenets:
1. Randomly choosing beneficiaries would assist prevent selection bias; each facility/site recipient should have an equal probability of being chosen (except in the case of over-sampling for a specific sub-group, although this is generally not recommended).
2. Make the sampling viable and reasonable. You must weigh accuracy against feasibility and price when choosing the sample, you should employ.
3. increase the sample size so that the results have the required level of precision.
Principal Steps in the Process of Sample Survey
We can understand the principal steps involved in Sample Survey as mentioned in the 11 steps below:
- Laying down the objectives for the whole survey to strategize appropriately.
- Defining the population units to correctly decipher the population size.
- Deleting non-relevant questions and defining expressly the questions to be included and the data to be collected.
- Prescribing the degree of precision required in the survey (based on the topic of research and varies in every case).
- Preparing the questionnaire is the next most crucial step. There must also be selection and relevant training of the data collectors.
- Selecting the sample design for the survey.
- Selecting the sample units and also defining the same for clarity purposes.
- Conducting a pre-test which ultimately leads to fruitful improvements in the questionnaire.
- Organising the fieldwork by way of quality check. A procedure must be established for early-checking of the quality requirements.
- Summarising and analysing the data thus collected forms the most basic yet intrinsic part of a survey whereby the result thus extracted is to be used in the research for structuring the whole research and thereby testing the hypotheses.
- Further, it is important to note that surveys apart from benefitting the research for which they are conducted, also provide information relevant to any further or future research which might use the said survey in the form of secondary information. So there must be relevant information provided by the survey so that its future use can be appropriately made.
Sampling can therefore be understood to be a precise way of researching in a manner that leads to accurate results with investments that are both time and money-saving. It is imperative to evaluate the type of research to be undertaken to infer the appropriate form of sampling to be used for the research.
 ‘The Principal Steps in a Sample Survey’, Available Here
 ‘Six Stages to Choose Sampling Techniques’, Available Here
 Shona McCombes, ‘Sampling Methods | Types, Techniques & Examples’, Available Here
 Aaron Moss, ‘What Is the Purpose of Sampling in Research?’, Available Here
 Methods of sampling from a population, Available Here