STUDY
AND
EXAM
.COM

Sampling

Sampling means the process of selecting a part of the population. A population is a group of people that is studied in research. These are the members of a town, a city or a country.  It is difficult for a researcher to study the whole population due to limited resources, e.g., time, money and energy. Hence, the researcher selects a part of the population for the study, rather than studying the whole population. This process is known as sampling. It makes the research manageable and convenient for the researcher.

The reliability of the findings of research depends on how well you select the sample. A sample should be a true representative of the whole population. It should include persons from various sections and spheres of the population in order to become a true representative of the population.

The terminologies relevant to sampling are as follows:

  1. Sample: The part of the population selected for the research is known as a sample.
  2. Sample Size: The number of people in the selected sample is known as sample size.
  3. Sampling Frame: Sampling frame means the list of individuals or people included in the sample. It reflects who will be included in the sample. For making a sampling frame, the researcher has to make a list of names and details (e.g., age and gender) of all the members of the sample.
  4. Sampling Technique: It refers to the technique or procedure used to select the members of the sample. There are various types of sampling techniques, as follows: 

  TYPES OF SAMPLING

There are two major types of sampling, i.e. Probability and Non-probability Sampling, which are further divided into sub-types as follows:

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. Snow-ball Sampling
  4. Quota Sampling

   PROBABILITY SAMPLING

Probability sampling is a type of sampling where each member of the population has a known probability of being selected in the sample. When a population is highly homogeneous, each of its members has a known chance of being selected in the sample. For example, if we pick some sugar grains from any part of the bag containing sugar, they will have similar characteristics. In such a case, each member has a known chance of being selected in a sample. Hence, the sample collected from any part of a bag containing sugar will be a true representative of the whole sugar in the bag. In such a situation, probability sampling is adopted. The extent of homogeneity of a population usually depends upon the nature of the research, e.g., who are the target respondents of the research. For instance, if a researcher wants to know community attitude towards some common and general phenomenon. For such a study, the whole population serves as relatively a homogeneous group as every member of the population can be the target respondents of the research. Therefore, a random sampling technique can be used.

The types of probability sampling are explained below:

   Simple Random Sampling

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.

   Stratified Random Sampling

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 enough homogenous so that a simple random sampling method can straight be used. Hence, the population is first divided into homogeneous sub-groups based on certain similarities of the members (e.g., age, sex, religion, 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.

   Systematic Sampling

As the name mentions, 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 the list of all individuals of the population and then decides to select every person occurring after some fixed interval on the list of all individuals. 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. We noted, here in this example, that the researcher selects member occurring after every interval of ten individual on the list. Therefore, the Kth element here is the 10th element. This means if the researcher has to select a sample from 100 individuals, each member occurring on the following number on the list will be selected for the sample.

Sample = {10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, 90th, 100th}

The Kth element (or the interval) depends on the size of the population and desired sample size. For example, if we want to select a sample of 20 members from the population of 1000 members. We will divide the total population over the desired sample, e.g., 1000/50 = 50. It means we will select every 50th member from the population to make a sample of 20 members.

   Cluster Sampling

In cluster sampling, various segments of a population are treated as clusters and members from each cluster are selected randomly. It seems similar to stratified sampling, but there is a difference in both. 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 but 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

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 then, from each smaller cluster members are selected randomly. Even the smaller cluster may be divided further into the smallest clusters depending upon the nature of the research.

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

   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, to study the impacts of domestic violence on children, the researcher may not interview all the children but will interview only those children who are suffered from domestic violence. 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:

   Purposive Sampling

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 drugs 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 for this study because they have undergone impacts of drug abuse on their health, and they can provide the real data for this study. Hence, the researcher may select only the drug addicts as respondents for his study.

   Convenience Sampling

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 visit a college or a university and get the questionnaires filled in by volunteer students. Similarly, a researcher may stand in a market and interview the volunteer persons.

   Snow-ball Sampling

Snow-ball sampling is also called chain sampling. It is a type of sampling where one respondent identifies (or suggests) other respondents (from his 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 the researcher locate other migrants to be interviewed. The respondents may tell the researcher about his 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 goes on growing like a snow-ball. The researcher continues this method until the required sample is achieved. 

   Quota Sampling

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.