Types of Sampling and ways to determine sample size

Sampling is the statistical process of selecting and studying the characteristics of a relatively small number of items from a relatively large population of such items to draw statistically valid inferences about the characteristics about the entire population. Learn more about sampling and the various methods of sampling.

It is important to have a group of people who will participate in the survey and be able to represent the whole target population. This group is called a “sample”. Sampling is a method of studying from a few selected items, instead of the entire big number of units.

The small selection is called sample. The large number of items of units of a particular characteristic is called population.

Example: We check a sample of rice to see whether the rice well boiled or not. We check a small sample of solution to decide how much a given solution is concentrated, and with the sample we infer about a population.

The primary strength of sampling is that it accurately estimates a population’s characteristics by surveying a small proportion of the population.

Methods of Sampling

The various sampling methods can be categorized as follows:

• Probability Sampling and
• Non-Probability Sampling

Probability sampling

Probability sampling is a sampling technique wherein the samples are gathered in a process that gives all the individuals in the population equal chances of being selected. The process is not based on the discretion of the researcher but is carried out in such a way that the probability of every unit in the population of being included is the same.

This sampling process is similar to a lottery: the entire population of interest could be selected for the survey, but only a few are chosen at random.

Researchers often use random-digit dialing to perform simple random sampling. In this procedure, telephone numbers are generated by a computer at random and called to identify individuals to participate in the survey.

Some characteristics of the probability sample are:

• There is an equal probability of each unit in the sample of entering the sample.
• In the analysis the weight appropriate to the probabilities are used.
• The process of sampling is automatic in one or ore steps of the selection of units in the sample.

There are various methods that can be used for probability sampling with each method having its own strengths and limitations. Some of these are as follows:

Simple Random Sampling

Simple random sampling is the basic sampling technique where we select a group of subjects (a sample) for study from a larger group (a population). Each individual is chosen entirely by chance and each member of the population has an equal chance of being included in the sample.

Systematic Sampling

Systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equal-probability method.

Stratified Sampling

Stratified samples are used when a researcher wants to ensure that there are enough respondents with certain characteristics in the sample.

The researcher first identifies the people / types of organisations in the population who have the desired characteristics, then randomly selects a sample of them.

With stratified sampling, the researcher divides the population into separate groups, called strata. Then, a probability sample (often a simple random sample) is drawn from each group.

Stratified sampling requires that adjustments be made in statistical analyses.

For example, a researcher may want to compare survey responses of private sector, and not-for-profit sector (NFPs) organisations. To ensure that there are enough NFPs in the survey, the researcher will first identify the NFPs in the population and then randomly select a sample of NFPs.

Proportionate Stratified Random Sampling

This method includes the sample having specified characteristics in exact proportion to those characteristics which are distributed in the population.

Disproportionate Stratified Random Sampling

With disproportionate stratification, the sample size of each stratum does not have to be proportionate to the population size of the stratum.

Cluster Sampling

Cluster sampling is generally used when it is geographically impossible to undertake a simple random sample. It is used when “natural” but relatively homogeneous groupings are evident in a statistical population.

Cluster sampling requires that adjustments be made in statistical analyses.

For example, in a face-to-face interview, it is difficult and expensive to survey businesses across the nation. Instead, researchers will randomly select geographic areas (for example, counties), then randomly select businesses within these areas. This creates a cluster sample, in which respondents are clustered together geographically.

Non-probability Sampling

A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques.

We can divide non-probability sampling methods into accidental or purposive and quota sampling. Most sampling methods are purposive in nature because we usually approach the sampling problem with a specific plan in mind.

Accidental Sampling

This category includes the traditional “man on the street” interviews conducted frequently by television news programmes to get a quick reading of public opinion.

Quota Sampling

In quota sampling, you select people non randomly according to some fixed quota.

Purposive Sampling

In Purposive Sampling, we sample with a purpose in mind. We usually would have one or more specific predefined groups we are seeking.

Snowball sampling

In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find.

For instance, if you are studying the homeless, you are not likely to be able to find good lists of homeless people within a specific geographical area. However, if you go to that area and identify one or two, you may find that they know very well who the other homeless people in their vicinity are and how you can find them.

Note: Non-random samples cannot be generalized to the population of interest. Consequently, it is problematic to make inferences about the population. In survey research, random, cluster, or stratified samples are preferable.

Determining Sample Size

Once you have decided the target population, you have to decide the number of the participants in a sample, known as the “sample size”. Besides the estimated number of people in the target population, sample size can be influenced by other factors such as budget, time available, and the target degree of precision.

sample size can be calculated using a formula:

Adhering to the sample size help achieve higher precision in the results because having participants less than the sample size leads to low representativeness of the target population. On the other hand, going much over the sample size may cause a diminished rate of enhancement in the precision of the survey outcomes.

Related: Understanding Validity and Reliability

Related: Research process explained