Sampling

Sampling refers to the process of selecting a subset of individuals from a larger population for the purpose of research. A population comprises all members of a defined group, such as the residents of a town, city, or country that are of interest in a study. Conducting research on an entire population is often impractical due to constraints such as time, financial resources, and energy. Therefore, researchers select a portion of the population to represent the whole. This process is known as sampling, and it allows the research to be more manageable and efficient.

The accuracy and reliability of research findings largely depend on the method of sample selection. A sample must be truly representative of the population, encompassing individuals from diverse sections and strata to ensure validity and generalizability of the results.

Key Terminologies in Sampling:

1. Sample: A subset of the population selected for participation in a research study.
2. Sample Size: The number of individuals included in the sample.
3. Sampling Frame: A detailed list of individuals or units from which the sample is drawn. This list identifies who is eligible to be included in the sample and typically includes relevant details such as age, gender, or other characteristics.
4. Sampling Technique: The method or procedure used to select members of the sample. Various sampling techniques are employed depending on the nature and objectives of the research.

  TYPES OF SAMPLING

Sampling techniques are broadly categorized into Probability Sampling and Non-Probability Sampling, each of which includes several subtypes.

1. Probability Sampling

1. Simple Random Sampling
2. Stratified Random Sampling
3. Systematic Sampling
4. Cluster Sampling
5. Multi-stage Sampling

2. Non-Probability Sampling

1. Purposive Sampling
2. Convenience Sampling
3. Snowball Sampling
4. Quota Sampling

   PROBABILITY SAMPLING

Probability sampling is a sampling technique in which each member of the population has a known and non-zero probability of being selected. This method is particularly appropriate when the population is relatively homogeneous, as each member then has an equal chance of inclusion in the sample. For instance, if a researcher randomly selects sugar grains from a bag, each grain possesses similar characteristics, and the selected sample accurately represents the whole. In such cases, probability sampling ensures that the sample reflects the population faithfully.

The degree of homogeneity within a population often depends on the objectives of the research and the characteristics of the target respondents. For example, when assessing general community attitudes toward a widely experienced phenomenon, the population may be treated as a relatively homogeneous group. Consequently, random selection methods are appropriate in such situations.

The types of probability sampling are explained below:

In simple random sampling, the members of the sample are selected randomly and purely by chance. As every member has an equal chance of being selected in the sample, a random selection of members does not affect the quality of the sample. Hence, the members are randomly selected without specifying any criteria for selection. Sometimes, the researcher may use a lottery system to select the members randomly. Simple random sampling is a suitable technique for a population that is highly homogeneous.

Example: A researcher wants examine the impact of the study habits of students on their academic performance at a college. He may randomly select 50 students from a list of all 500 enrolled students using a random number generator. Each student has an equal chance of being selected, ensuring a representative sample.

In stratified random sampling, the population is first divided into sub-groups (known as strata) and then members from each sub-group are selected randomly. This technique is adopted when the population is homogeneous but not sufficiently homogeneous for a simple random sampling method to be directly used. Hence, the population is first divided into homogeneous sub-groups based on certain similarities of the members (e.g., age, sex, religion, and ethnicity). Then, members from each sub-group are randomly selected. The purpose is to address the issue of less homogeneity of the population and to make a true representative sample.

Example: A researcher studying employee satisfaction in a company may divide employees into sub-groups based on specific factors, such as gender or job rank, and then randomly select respondents from each sub-group. This approach ensures that the sample accurately represents all employees. If these variations are not considered and participants are selected purely at random, there is a risk that the sample may consist predominantly of individuals from a single category (e.g., one gender or job rank). Such a sample could produce biased results, as employees in different genders or ranks may have distinct experiences and perspectives.

As the name suggests, this type of sampling follows a systematic pattern for selecting members of the sample. In systematic sampling, a member occurring after a fixed interval is selected for the sample. The researcher makes a list of all individuals in the population and then decides to select every person occurring after some fixed interval on the list. For instance, the researcher may decide to select a person occurring after every ten individuals on the list. This number occurring after every fixed interval is known as the Kth element.

Example 1: If a researcher wants to survey 10 students from a class of 100, they may select every 10th student on the attendance list. The resulting sample would include the 10th, 20th, 30th, and so on, up to the 100th student.

Example 2: For a survey of 20 households in a neighborhood of 1000, the researcher calculates the interval K = 1000 ÷ 20 = 50 and selects every 50th household on the list to achieve a representative sample.

In cluster sampling, various segments of a population are treated as clusters, and members from each cluster are selected randomly.

For instance, a researcher studying household dietary habits may treat each family within a community as a cluster and randomly select households to survey. Similarly, a study on education quality may treat each town within a district as a cluster and randomly select schools from each town.

Cluster sampling may seem similar to stratified sampling, but there is a difference between the two. In stratified sampling, the researcher intentionally divides the population into homogeneous sub-groups based on similar characteristics, e.g., age, sex, profession, or religion. On the other hand, in cluster sampling, the researcher does not intentionally divide the population into sub-groups; instead, there are already existing or naturally occurring sub-groups (or clusters) within the population, e.g., families within a society, towns within a district, organizations within a city, and so on. These already existing or naturally occurring sub-groups are treated as clusters, and members are randomly selected from these clusters. For instance, a researcher may treat each family within a community as a cluster. Similarly, a researcher may treat each town within a district as a cluster.

Multi-stage sampling is a complex form of cluster sampling. In multi-stage sampling, each cluster of the sample is further divided into smaller clusters, and members are selected from each smaller cluster randomly. It is called multi-stage sampling because it involves two or more stages. First, naturally occurring groups in a population are selected as clusters; then each cluster is divided into smaller clusters, and from each smaller cluster, members are selected randomly. Even the smaller clusters may be divided further into the smallest clusters depending upon the nature of the research.

For example, a researcher studying public health in a country might first select states as primary clusters, then select districts within each state as secondary clusters, and finally select households within those districts as the final units for data collection. Multi-stage sampling allows researchers to manage large populations efficiently while ensuring representativeness.

It should be noted that the name ‘multi-stage sampling’ is also sometimes used for sampling procedures involving other techniques where two or more stages are involved.

   NON-PROBABILITY SAMPLING

Non-probability sampling is a type of sampling where each member of the population does not have a known probability of being selected in the sample. In this type of sampling, each member of the population does not get an equal chance of being selected in the sample. Non-probability sampling is adopted when each member of the population cannot be selected, or the researcher intentionally wants to choose members selectively.

For example, in a study examining the impacts of domestic violence on children, the researcher may interview only children who have experienced domestic violence, using judgment to select participants. Hence, the members cannot be selected randomly. The researcher will use his judgment to select the members.

The types of non-probability sampling are explained as below:

It is a type of sampling where the members of a sample are selected according to the specific purpose of the study.

For example, if a researcher wants to study the impact of drug abuse on health, every member of the population cannot be the best respondent for this study. Only the drug addicts can be the best respondents because they have undergone the impacts of drug abuse on their health and can provide the real data for this study. Hence, the researcher may select only the drug addicts as respondents for the study.

It is a type of sampling where the members of the sample are selected based on their convenient accessibility. Only those members are selected who are easily accessible to the researcher. For example, a researcher may distribute questionnaires to volunteer students at a college or interview shoppers who are readily available in a market.

Snow-ball sampling is also called chain sampling. It is a type of sampling where one respondent identifies (or suggests) other respondents (from their friends or relatives) for the study. Snow-ball sampling is adopted in situations where it is difficult for the researcher to identify the members of the sample.

For example, a researcher wants to study ‘problems faced by migrants in an area.’ The researcher may not know enough number of migrants in the area to collect data from them. In such a case, the researcher may ask one migrant respondent to help locate other migrants to be interviewed. The respondents may tell the researcher about other friends who are also migrants in the area. Similarly, the new respondents (identified by the last respondent) may suggest some other new respondents. In this way, the sample grows like a snowball. The researcher continues this method until the required sample is achieved.

In this type of sampling, the members are selected according to some specific characteristics chosen by the researcher. These specific characteristics serve as a quota for the selection of members of the sample. Hence, the members are selected based on these specific characteristics, such as age, sex, religion, profession, ethnicity, interest, and so on.

For instance, if a researcher is studying consumer preferences in a city, they may select 50 men and 50 women across different age groups to ensure the sample reflects the population structure.