Sampling Methods Overview PDF

SAMPLING METHODS  PROBABILITY SAMPLING Probability Sampling is a sampling technique in which sample from a larger population are chosen using a method based on the theory of probability. The most important requirement of probability sampling is that everyone in your popula...

SAMPLING METHODS  PROBABILITY SAMPLING Probability Sampling is a sampling technique in which sample from a larger population are chosen using a method based on the theory of probability. The most important requirement of probability sampling is that everyone in your population has a known and an equal chance of getting selected. For example, if you have a population of 100 people every person would have odds of 1 in 100 for getting selected. 1. Simple Random Sampling This sampling method is as easy as assigning numbers to the individuals (sample) and then randomly choosing from those numbers through an automated process. There are two ways in which the samples are chosen in this method of sampling: Lottery system and using number generating software/ random number table. 2. Stratified Sampling It involves a method where a larger population can be divided into smaller groups. A common method is to arrange or classify by sex, age, ethnicity and similar ways. Splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups. 3. Cluster Sampling Cluster Sampling is a way to randomly select participants when they are geographically spread out. For example, if you wanted to choose 100 participants from the entire population of the U.S., it is likely impossible to get a complete list of everyone. Instead, the researcher randomly selects areas (i.e. cities or counties) and randomly selects from within those boundaries. Cluster sampling usually analyzes a particular population in which the sample consists of more than a few elements, for example, city, family, university etc. 4. Systematic Sampling It is when you choose every “nth” individual to be a part of the sample. For example, you can choose every 5th person to be in the sample.  NON-PROBABILITY SAMPLING Non-probability sampling is a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. In non-probability sampling, not all members of the population have a chance of participating in the study. Non-probability sampling is most useful for exploratory studies. Non-probability sampling is used in studies where it is not possible to draw random probability sampling due to time or cost considerations. This sampling method depends heavily on the expertise of the researchers.  1. Convenience Sampling It is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to researcher. These samples are selected only because they are easy to recruit and researcher did not consider selecting sample that represents the entire population. An example of convenience sampling would be using student volunteers known to researcher. Researcher can send the survey to students and they would act as sample in this situation. There are no criteria that need to be considered to be a part of this sample and due to which it becomes extremely simplified to include elements in this sample. Every element of the population is eligible to be a part of this sample and is dependent on the proximity to the researcher to get included in the sample. 3. Quota Sampling The researcher wants to study the career goals of male and female employees in an organization. There are 500 employees in the organization. These 500 employees are known as population. The researcher is interested in particular strata within the population. For studying the career goals of 500 employees, technically the sample selected should have proportionate numbers of males and females. The researchers can form a sample involving individuals that represent a population and are chosen according to traits or qualities. 4. Judgmental/Purposive Sampling The samples are selected based purely on researcher’s knowledge and credibility. In other words, researchers choose only those who he feels are a right fit (with respect to attributes and representation of a population) to participate in research study. Purposive sampling is used where there is time-constraint for sample creation and the authorities involved would prefer relying on their knowledge and not on other sampling method. 5. Snowball Sampling Chain-referral sampling Researchers use this technique when the sample size is small and not easily available. For example, this type of sampling can be used to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer other subjects suffering from the same ailment to form a subjective sample to carry out the study. 5. Consecutive Sampling The researcher picks a single person or a group of sample, conducts research over a period of time, analyzes the results and then moves on to another subject or group of subject if needed. It gives the researcher a chance to work with many subjects and fine tune his/her research by collecting results that have vital insights. Here, a researcher can accept the null hypothesis, if not the null hypothesis, then its alternative hypothesis and if neither of them is applicable then a researcher can select another pool of samples and conduct the research or the experiment once again before finally making a research decision.