Sampling

Sampling is a fundamental aspect of
statistics, but unlike the other methods of data collection, sampling involves
choosing a method of sampling which further influences the data that you will result
with. There are two major categories in sampling: probability and non-probability
sampling.

Probability Sampling

Under probability sampling, for a given population, each element of that population
has a chance of being picked to part of the sample. In other words, no single element
of the population has a zero chance of being picked

The odd/chances/probability of picking any element is known or can be calculated.
This is possible if we know the total number in the entire population such that
we are then able to determine that odds of picking any one element.

Probability sampling involves random picking of elements from a population, and
that is the reason as to why no element has a zero chance of being picked to be
part of a sample.

Methods of Probability Sampling

There are a number of different methods of probability sampling including:

1. Random Sampling

   Random sampling is the method that most closely defines probability sampling. Each
   element of the sample is picked at random from the given population such that the
   probability of picking that element can be calculated by simply dividing the frequency
   of the element by the total number of elements in the population. In this method,
   all elements are equally likely to be picked if they have the same frequency.

2. Systematic Sampling

   Systematic sampling is the method that involves arranging the population in a given
   order and then picking the nth element from the ordered list of all the elements
   in the population. The probability of picking any given element can be calculated
   but is not likely to be the same for all elements in the population regardless of
   whether they have the same frequency.

3. Stratified Sampling

   Stratified sampling involves dividing the population into groups and then sampling
   from those different groups depending on a certain set criteria.

   For example, dividing the population of a certain class into boys and girls and
   then from those two different groups picking those who fall into the specific category
   that you intend to study with your sample.

4. Cluster Sampling

   Cluster sampling involves dividing up the population into clusters and assigning
   each element to one and only one cluster, in other words, an element can’t appear
   in more than one cluster.

5. Multistage Sampling

   Multistage sampling involves use of more than one probability sampling method and
   more than one stage of sampling, for example for using the stratified sampling method
   in the first stage and then the random sampling method in the second stage and so
   on until you achieve the sample that you want.

6. Probability Proportional to Size Sampling

   Under probability proportional to size sampling, the sample is chosen as a proportion
   to the total size of the population. It is a form of multistage sampling where in
   stage one you cluster the entire population and then in stage two you randomly select
   elements from the different clusters, but the number of elements that you select
   from each cluster is proportional to the size of the population of that cluster.

Non-Probability Sampling

Unlike probability sampling, under non-probability sampling certain elements of
the population might have a zero chance of being picked. This is because we can’t
accurately determine the chances/probability of picking a given element so we do
not know whether the odds of picking that element are zero or greater than zero.
Non-probability sampling may not always be a consequence of the sampler’s ignorance
of the total number of elements in the population but may be a result of the sampler’s
bias in the way he chooses the sample by excluding some elements.

Methods of Non-Probability Sampling

There are a number of different methods of Non-probability sampling which include:

1. Quota Sampling

   Quota sampling is similar to stratified sampling only that in this case, after the
   population is divided into groups, the elements are then sampled from the group
   using the sampler’s judgement and as a consequence the method loses any aspect of
   being random and can be extremely biased.

2. Accidental or Convenience Sampling

   Accidental sampling is a method of sampling where by the sampler picks the sample
   based on the fact that the elements that he/she picks are conveniently close at
   the moment. For example, if you walked down the street and sampled the first ten
   people you meet, the fact that they happened to be there is convenient for you but
   accidental for them which leads to the name of the method.

3. Purposive or Judgemental Sampling

   Purposive or judgemental sampling is a method of sampling where by the sampler picks
   the sample from the entire population solely based on the his/her judgement. The
   sampler controls to a very large extend which elements have a chance of being selected
   to be in the sample and which ones don’t.

4. Voluntary Sampling

   Voluntary sampling, as the name suggests, involves picking the sample based on which
   elements of the population volunteer to participate in the sample. This is the most
   common method used in research polls.

5. Snowball Sampling

   Snowball sampling is a method of sampling that relies on referrals of previously
   selected elements to pick other elements that will participate in the sample.