According to Blalock most sampling methods could be classified into two categories
(i) Non probability sampling method
(ii) Probability sampling method
Non-Probability Sampling Methods
Non probability sampling is one in which there is no way of assessing the probability of the element or group of elements, of population being included in the sample. Important techniques of non-probability sampling methods are:
- Haphazard, Accidental or Convenience Sampling: Haphazard sampling can produce ineffective, highly unrepresentative samples and is not recommended. When a researcher haphazardly selects cases that are convenient, he or she can easily get a sample that seriously misrepresents the population. For example, An investigator may take student of class X into research plan because the class teacher of the class happens to be his/her friend. This illustrates accidental or convenience sampling.
- Quota Sampling: Quota sampling is an improvement over haphazard sampling. In quota sampling, a researcher first identifies relevant categories of people (e.g. male and female or under age 30, ages 30 to 60, over age 60 etc.) then decides how many to get in each category. Thus, the number of people in various categories of the sample if fixed. Quota sampling ensures that some differences exist in the sample. In haphazard sampling, all those interviewed might be of the same age, sex or background.
- Purposive Sampling: Purposive sampling is a valuable kind of sampling for special situations. It is used in exploratory research or in field research. It uses the judgement of an expert in selecting cases or it selects cases with a specific purpose in mind. With purposive sampling, the researcher never knows whether the cases selected represent the population. Purposive sampling is appropriate to select unique cases that are especially informative.
- Snowball Sampling: Snowball sampling is also known as network, chain referral or reputation sampling method. Snowball sampling which is a non profitability sampling method is basically sociometric. It begins by the collection of data on one or more contacts usually known to the person collecting the data. At the end of the data collection process (e.g. questionnaire, survey or interview), the data collector asks the respondent to provide contact information for other potential respondents. These potential respondents are contacted and provided more contacts. Snowball sampling is most useful when there are very few methods to secure a list of the population or when the population is unknowable.
- Systematic Sampling: Systematic sampling is another method of non probability sampling plan, thought the label ‘systematic’ is somewhat misleading in the sense that all probability sampling methods are systematic sampling methods. Due to this, if often sounds that systematic sampling should be included under one category of probability sampling, but in reality this is not the case.
Probability Sampling Methods
Probability sampling methods are those that clearly specify the probability or likelihood of inclusion of each element or individual in the sample. Probability sampling is free of bias in selecting sample units.
They help in estimation of sampling errors and evaluate sample results in terms of their precision, accuracy and efficiency. Hence, the conclusions reached from such samples are worth generalisation and comparable to similar population to which they belong.
Major probability sampling methods are:
- Simple Random Sampling: A simple random sample requires:
– A complete listing of all the elements.
– An equal chance for each elements to be selected.
– A selection process whereby the selection of one element has no effect on the chance of selecting another element.
For example, If we are to select a sample of 10 students from the seventh grade consisting of 40 students, we can write the names (or roll number) of each of the 40 students on separate slips of paper, all equal in size and color and fold them in a similar way. Subsequently, they may be placed in a box and reshuffled thoroughly.
- Stratified Random Sampling: In stratified random sampling the population is divided into two or more strata, which may be based upon a single criterion such as sex, yielding two strata – male and female or upon a combination of two or more criteria such as sex and graduation, yielding four strata, namely, male undergraduates, male graduates, female undergraduates and female graduates.
These divided populations are called sub-population which are non-overlapping and together constitute the whole population.
- Cluster Sampling: A type of random sample that uses multiple stages and is often used to cover wide geographic areas in which aggregated units are randomly selected and them sample are drawn from the samples aggregated units or cluster.