Sampling methods are as follows;

  • Probability Sampling
  • Non-Probability Sampling

Probability Sampling:

A probability sampling scheme is one in which each unit in the population has a chance (greater than zero) of being selected in the sample, and this possibility can be accurately determined.

The combination of these behaviours makes it possible to produce unbiased estimations of population totals, by weighting sampled units rendering to their probability of selection.

Probability sampling may be of the following types:

Simple random sampling

In a simple random sample ('SRS') of a given size, all such subsets of the frame are given an equal probability. Each component of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. This minimizes bias and simplifies analysis of results. 

Systematic sampling

Systematic sampling depend on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards.

In this case, k=(population size/sample size).

It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list.

 A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10').

Stratified sampling

The sampling where the population embraces several distinct categories, the frame can be organized by these categories into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. Dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample.

Cluster sampling

It is an example of 'two-stage sampling' or 'multistage sampling': in the first stage a sample of areas is chosen; in the second stage a sample of respondents within those areas is selected.

Multistage sampling

Multistage sampling is a complex form of cluster sampling in which two or more levels of units are embedded one in the other. The first stage consists of constructing the clusters that will be used to sample from. In the second stage, a sample of primary units is randomly selected from each cluster (rather than using all units contained in all selected clusters). In following stages, in each of those selected clusters, additional samples of units are selected, and so on. All ultimate units (individuals, for instance) selected at the last step of this procedure are then surveyed. This technique, thus, is essentially the process of taking random samples of preceding random samples. It is not as effective as true random sampling, but it probably solves more of the problems inherent to random sampling. Moreover, It is an effective strategy because it banks on multiple randomizations. As such, it is extremely useful. Multistage sampling is used frequently when a complete list of all members of the population does not exist and is inappropriate. Moreover, by avoiding the use of all sample units in all selected clusters, multistage sampling avoids the large, and perhaps unnecessary, costs associated traditional cluster sampling.


Non-Probability Sampling:

Non-probability sampling is any sampling technique where some elements of the population have no definite chance of selection, or where the probability of selection can't be correctly determined.

Non-probability sampling may be of the following types:

Quota sampling

in quota sampling the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion. It is this second step which makes the technique one of nonprobability sampling. In quota sampling the selection of the sample is non-random. For example, interviewers, might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years.

Convenience sampling (grab or opportunity sampling):

Convenience sampling is a type of non-probability sampling which involves the sample being drawn from that part of the population which is close to hand? That is, a sample population selected because it is readily available and convenient. It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone. The researcher using such a sample cannot scientifically generalize about the total population from this sample because it would not be representative enough. For example, if the interviewer was to conduct such a survey at a shopping centre early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing.

business research methods

August 22, 2017