What are Sampling Methods and How to Select One for You?

Normally, it would be impractical to study an entire population, such as when conducting a questionnaire survey. Sampling is a technique that allows researchers to infer information about a population based on results from a subset of the population rather than investigating every individual. 

 

Reducing the number of participants in a study lowers costs and workload while also making it easier to obtain high-quality data, but this must be balanced against having a large enough sample size with enough power to detect a true association.

 

If a sample is to be used, it is critical that the individuals chosen are representative of the entire population, regardless of the method used. This may entail specifically targeting difficult-to-reach groups.

 

For example, if a drug manufacturer wants to investigate the negative side effects of a drug on the country's population, it is nearly impossible to conduct a research study that includes everyone. 

 

In this case, the researcher selects a sample of people from each demographic and then conducts research on them, providing him or her with preliminary feedback on the drug's behavior.

 

Also Read | Types of Research Methods

 

 

Why is the Sampling Method Important for Researchers?

 

Everyone who has ever worked on a research project understands that resources are finite; time, money, and people are never in infinite supply. 

 

As a result, rather than collecting data from the entire population, most research projects aim to collect data from a sample of people (the census being one of the few exceptions). This is due to the fact that sampling enables scientists to:

 

1. Time is Money

 

It takes time to contact everyone in a population. And, invariably, some people will not respond to the initial attempt to contact them, requiring researchers to invest more time in follow-up. 

 

Random sampling is much faster than surveying the entire population, and non-random sampling is almost always faster than random sampling. As a result, sampling saves researchers a significant amount of time.

 

 

2. Spend Less Money

 

The number of people contacted by a researcher is directly proportional to the cost of a study. Sampling saves money by allowing researchers to obtain the same answers from a sample as they would from the entire population.

 

Non-random sampling is significantly less expensive than random sampling because it reduces the cost of finding people and collecting data from them. Saving money is important because all research is done on a budget.

 

 

3. Amass More Information

 

Sometimes the goal of the research is to collect a small amount of data from a large number of people (e.g., an opinion poll). At times, the goal is to gather a large amount of information from a small group of people (e.g., a user study or ethnographic interview).

 

In either case, sampling enables researchers to ask participants more questions and collect more detailed data than contacting everyone in a population.

 

Also Read | Hypothesis Testing

 

 

Importance of Selecting the Appropriate Sampling Method

 

A significant research result is obtained through sampling. However, sample errors can occur due to the differences that can exist between a population and a sample. As a result, it is critical to employ the most relevant and useful sampling method.

 

Three of the most common sampling errors are listed below.

 

• When a sample does not accurately reflect the characteristics of the population, this is referred to as sampling bias.

 

• When the wrong subpopulation is used to select a sample, sample frame errors occur. This could be due to factors such as gender, race, or economic status.

 

• When the results of a sample differ significantly from the results of the population, this is referred to as systematic error.

 

Also Read | What is Sampling Distribution?

 

 

How to Select the perfect Sampling Method?

 

It is unlikely that the researcher will be able to collect data from all cases in order to answer the research questions. As a result, a sample must be chosen. The population refers to the entire set of cases from which the researcher's sample is drawn. 

 

Because researchers do not have the time or resources to analyze the entire population, they use sampling techniques to reduce the size of the population. Below are the stages that are likely to occur during the sampling process.

 

1. Define the target population clearly

 

The first step in the sampling process is to define the target population precisely. The number of people living in a country is commonly referred to as its population.

 

2. Choose a Sampling Frame

 

A sampling frame is a list of real-world cases from which a sample will be drawn. The sampling frame must be population-representative.

 

3. Select a Sampling Technique

 

Prior to delving into the various types of sampling methods, it is important to define sampling and the reasons why researchers are likely to select a sample. Sampling is the process of selecting a subset from a predetermined sampling frame or the entire population. 

 

Sampling can be used to draw conclusions about a population or to make generalizations about existing theory. In essence, this is determined by the sampling technique used.

 

In general, sampling techniques are classified as one of two types:

 

1. Random sampling or probability

 

2. Non-probability sampling, also known as non-random sampling

 

Before deciding on a specific type of sampling technique, a broad sampling technique must be chosen.

 

 

Types of Sampling Methods

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Sampling Methods

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1. Probability Sampling

 

Random selection is used in the probability sampling method. In this method, all eligible individuals have a chance to choose a sample from the entire sample space. This method is more time-consuming and costly than non-probability sampling. 

 

The advantage of using probability sampling is that it ensures that the sample is representative of the population. There are several types of probability sampling methods, including simple random sampling, systematic sampling, stratified sampling, and clustered sampling. 

 

Let us now go over the various types of probability sampling methods in detail, with illustrative examples.

 

• Simple random sampling

 

The Simple Random Sampling method is one of the best probability sampling techniques for saving time and resources. 

 

It is a reliable method of gathering information in which every single member of a population is chosen at random, purely by chance. Each individual has the same chance of being chosen to be a part of a sample.

 

For example, in a 500-person organization, if the HR team decides to conduct team-building activities, it is highly likely that they will prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal chance of being selected.

 

• Systematic sampling

 

The items are chosen from the target population using the systematic sampling method by selecting a random selection point and then using the other methods after a fixed sample interval. It is calculated by dividing the total population size by the population size desired.

 

Example: Assume that the names of 300 students at a school are arranged in reverse alphabetical order. To select a sample in a systematic sampling method, we must choose 15 students at random from a starting number of, say, 5. 

 

From number 5 onwards, every 15th person from the sorted list will be chosen. Finally, we'll have a sample of some students.

 

• Stratified sampling

 

Random selection within predefined groups is used in stratified sampling. It's helpful when researchers understand the target population and can decide how to subdivide (stratify) it in a way that makes sense for the research.

 

Also Read | Top 5 Statistical Data Analysis Techniques

 

 

2. Non-probability Sampling Methods

 

Non-probability sampling methods do not provide the same bias-removal benefits as probability sampling, but they are sometimes used for convenience or simplicity. The following are some examples of non-probability sampling and how they work.

 

For example, if you were studying travel habits in a group of people, it might be useful to separate those who own or use a car from those who rely on public transportation.

 

Stratified sampling has advantages, but it also raises the issue of how to stratify a population, which increases the risk of bias.

 

• Cluster sampling

 

Cluster sampling is a research technique in which researchers divide the entire population into sections or clusters that represent a population. 

 

Based on demographic parameters such as age, gender, location, and so on, clusters are identified and included in a sample. This makes it very easy for the creator of a survey to derive effective inference from the feedback.

 

For example, if the US government wants to assess the number of immigrants living on the US mainland, they can divide it into clusters based on states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii, and so on. 

 

This method of surveying will be more effective because the results will be organized by state and will provide insightful immigration data.

 

• Convenience sampling

 

This method is dependent on the ease of access to subjects, such as surveying mall customers or passing by on a busy street. Because of the ease with which the researcher can carry it out and contact the subjects, it is commonly referred to as convenience sampling. 

 

Researchers have almost no authority over the sample elements, which are chosen solely on the basis of proximity rather than representativeness. When time and money are limited, this non-probability sampling method is used to collect feedback. Convenience sampling is used when resources are limited, such as in the early stages of research.

 

For example, startups and non-governmental organizations (NGOs) usually conduct convenience sampling at a mall to distribute leaflets about upcoming events or to promote a cause – they do this by standing at the mall entrance and randomly handing out pamphlets.

 

• Quota sampling

 

This method, like the probability-based stratified sampling method, aims to achieve a spread across the target population by specifying who should be recruited for a survey based on specific groups or criteria. For example, your quota could include a certain number of males and females or people in specific age groups or ethnic groups.

 

Bias may be introduced during the selection process; for example, volunteer bias may skew the sample toward people with free time who want to participate. Alternatively, bias could be a by-product of how researchers choose categories for quotas.

 

• Snowball or referral sampling

 

People recruited to be part of a sample are asked to invite their friends and family, who are then asked to invite their friends and family, and so on. Like a snowball rolling downhill, participation spreads through a community of connected individuals.

 

When the researcher knows little about the target population and has no easy way to contact or access them, this method can be useful. It will, however, introduce bias, such as missing out on isolated members of a community or skewing towards certain age or interest groups who recruit amongst themselves.

 

• Judgmental or purposive sampling

 

The researcher's discretion determines whether to form judgmental or purposive samples. The purpose of the study, as well as an understanding of the target audience, are the only considerations for researchers. 

 

For example, when researchers want to understand the thought processes of people interested in pursuing a master's degree. "Are you interested in doing your masters in...?" will be the selection criteria. Those who respond with a "No" are removed from the sample.

 

• Consecutive sampling

 

With a minor exception, consecutive sampling is similar to convenience sampling. For sampling, the researcher selects a single person or a group of people. The researcher then conducts research for a period of time in order to analyze the results and, if necessary, move to another group.

 

Also Read | Bootstrapping Method: Types, Working and Applications
 

Statistical organizations prefer probability random sampling. In business, companies and marketers rely on non-probability sampling for their research. The researcher prefers this method because it allows him to obtain confidence from his respondents, particularly in business sample surveys such as the consumer price index. 

 

With the variety of samples available, probability random sampling is preferable because the researcher generates his data for the use of the entire population by using a probabilistic method to control bias during the sampling.

 

This method is based on evidence generated by statistical agencies that non-probability techniques are based on the purpose that leads to assumptions, resulting in risk. Based on assumptions, one will produce an incorrect generalization of the population.