Sampling is simply a process for obtaining relevant information and making inferences about a population by analysing a small group of people within the population for the purpose of a research. It essentially involves selecting a small portion from the aggregate or total population and examining that portion in order to draw inferences about the total population.
Population or Universe – It is the subject matter of research study. It refers to the entire group or population of something taken into consideration for the purpose of research. It may be finite or infinite.
Sample – A sample is that portion of the population which is critically analysed during a research study in order to make estimations or draw inferences about the entire population. A sample may be defined as a unit chosen from the entire population which represents all the features or characteristics of the entire population.
Sampling Unit – It refers to one item of a sample. It may be one unit of anything i.e. one consumer, one company, one state, one city etc.
Sampling Frame – The collection of all the items or units of a sample make up the sampling frame. It consists a list of all the items in a universe (only in case of finite universe, where it is possible to list down all items).
Sampling Design – It is simply a plan for obtaining a sample out of a given population. It lays down a definite plan for obtaining a sample out of the entire universe in terms of sampling objectives, population, sample frame, sample size, sample unit, data collection etc. It is determined before the step of data collection in order to obtain reliable, relevant and adequate information.
There are two ways in which information can be obtained for sampling:
Census Survey – When the entire population or universe is taken into consideration for the purpose of research.
Sample Survey – When only a part of population (sample) is studied.
Sample Size – It is the number of observations that form a sample i.e. the number of items that are selected from the entire population for the purpose of research that form a sample. It is denoted by n. The following points must be kept in mind while selecting a sample size:
Optimum – It must be optimum in size – Not too large, nor too small.
Representative – It must represent the entire population.
Reliable – It must meet the parameters of interest of the research study.
Sampling Errors – It refers to the inaccuracy or errors in the process of collection, analysis and interpretation of sampling data.
Sampling errors arise due to two reasons:
Systematic or biased or Non-sampling errors – These arise due to use of faulty procedures and techniques in making a sample and lack of experience in research.
Unsystematic or unbiased or sampling errors – These arise due to the limitations of the sampling process.
Sampling Errors – Sampling errors arise as we study only a small portion of the entire population to draw inferences about the whole population. Hence, there are random variations in the sample values as compared to population values. However if we study the entire population it is believed that errors will be nil. This also means the larger the sample size the smaller the sampling error i.e. sampling error is inversely proportional to the size of sample.
Non-Sampling errors – These errors result due to the following reasons:
Incorrect sampling frame or source list
Incorrect data collection techniques
Bias responses of respondents
Non-responses and omission errors
Errors in coding, tabulating, analysing data
Lack of trained and qualified investigators
Types of Sampling
Probability Sampling – In this type of sampling the probability of each item in the universe to get selected for research is the same. Hence the sample collected through this method is totally random in nature.
Non-Probability Sampling – In this type of sampling the probability of each item in the universe to get selected for research is not the same. Hence the sample collected through method is not random in nature.
Also read: Methods/Techniques of Sampling
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