Session 6 – Sampling methods

**Want to study the political views of Indian college students: Use stratified sampling**

If a researcher were studying political views among college students in India, it would be nearly impossible to survey every single college student across the country. With over 20 million students enrolled in more than 35,000 colleges, surveying the entire population would be extremely time consuming and costly. As a result, researchers use samples as a way to gather data.

A student sample will be a subset of the population (entire college students population) being studied. It will fairly represent the Indian college students’ population and will be used to draw inferences about that population.

A researcher will use sampling – a research technique widely used in the social sciences as a way to gather information about a population without having to measure the entire population.

In order to select a sample of the college students’ population, a researcher can use **probabilistic and non-probabilistic** techniques. First we will explore four

non- probabilistic sampling techniques such as convenience, purposive, snowballing and quota sampling.

If the researcher uses **convenience sampling** – the researcher approaches the college students near his/home or at a mall or on the street to participate in his/her study. The researcher should be cautious while using this approach as there are chances that he might generalize the results of convenience sampling to the general population. This generalization may or may not hold true as one knows that this will not be a fair representation of the college student population.

If a researcher uses **purposive sampling** – the researcher will approach colleges where the students express their political opinion freely. This indicates that the students are aware about the political scenario. The researcher is using a purposive sample because those being interviewed fit a specific purpose or description.

If a researcher uses **snowballing sampling** – the researcher may approach a college student and in turn ask him/her for a students’ reference. This process continues until the researcher has all the interviews he or she needs or until all contacts have been exhausted. A snowball sample is one in which the researcher collects data on the few members of the target population he or she can locate, then asks those individuals to provide information needed to locate other members of that population whom they know.

If a researcher used **quota sampling** – the researcher might need to know what proportion of the student college population is male and what proportion is female as well as what proportions of each gender fall into different age categories (18-21 years), race or ethnic categories (Hindu, Muslim, Parsi, Sikh, etc), educational categories (B.Com, B.A, Engg, MCM, Science), etc. The researcher would then collect a sample with the same proportions as the college student population.

Moving onto, **probability sampling techniques**, probability sampling is a sampling technique where the samples are gathered in a process that gives all the individuals in the population equal chances of being selected.

If a researcher used **random sampling – **with a population of 20 million college students (population), the researcher selects a sample of 5% (i.e. 1 million students). The 20 million students will be assigned a number. Those students assigned the random number will be selected in the sample. Subsequently, one million students (i.e. 5% of the college student population) are selected by generating a list of random numbers on the computer.

If a researcher used **systematic sampling** – with a population of 20 million college students, the researcher is looking at a sample of 1 million students. He will put the students in a list form and every 20^{th} student will be selected in the sample for inclusion. To ensure against any possible human bias in this method, the researcher should select the first individual at random. This is technically called a systematic sample with a random start.

If a researcher used **stratified sampling** – to obtain a stratified sample of college students, the researcher will first organize the population by study stream (B.Com, Engg, Science, Arts) and college class (1^{st} year, 2^{nd} year, 3^{rd} year, 4^{th} year). This ensures that the researcher has adequate number of subjects from each class in the final sample.

This type of sampling is used when the researcher wants to highlight specific subgroups within the population.

If a researcher used **cluster sampling** – Cluster sampling may be used when it is either impossible or impractical to compile an exhaustive list of the elements that make up the target population. In case of college students, we will have an exhaustive list of students from the university records. Hence, cluster sampling will not be used.

Now the researcher has to decide which sampling method should be used. He has to create a fine balance between the objective of the research and proper sample selection technique.

A look at the above mentioned techniques indicate that random sampling, stratified sampling, convenience and snowballing can be used for this study. These techniques will give him results pertaining to his research objective. However, stratified sampling will help him draw better and deeper inferences from the data set.