Sampling designs- Probability and non-probability sampling

Sampling designs are basically of two types
Propability Sampling and Non-Probability Sampling
 Probability sampling
A Probability sampling method is any method of sampling that utilize some forms of random selection.
Stages of Probability Sampling
Probability sampling method comprises four stages
1. Identifying from an appropriate sampling from based on your research question
and observation.
2. Determining a suitable sample size
3. choosing the most appropriate sampling technique and selecting sample.
4. Checking if the sample is representative of the population
Advantages and Disadvantages of probability sampling

Types of Probability sampling
  Simple Random Sampling (SRS)
  Stratified Sampling.
  Cluster Sampling.
  Systematic Sampling.
  Multistage Sampling (in which some of the methods above are combined in stages)
 Simple random sample
Simple random sample is the purest and most straight forward probability sampling
strategy. It is also most popular method for choosing sample among population for
wide range of purposes. In this method each member of population is equally
likely to be chosen as part of sample. It has been stated that the logic behind simple
random sample is that it removes bias from the selection procedure and should
result in representative sample. Identifying the size of more than few hundred is required in order to be able to apply simple random sampling in an appropriate
manner. It can be argued that simple random sampling is easy to understand a
theory but difficult to perform a practical

Stratified Sampling
A stratified sample is obtained by taking samples from each stratum or sub-group
of a population.
• Each stratum is represented proportionally according to its incidence in the
population.
• Each stratum must be non-overlapping as overlapping subgroups will mean
some members of the population will have higher chances of being selected.
• Most common strata are age, gender, socio-economic status, religion,
nationality, educational attainment.
• Simple Random Sampling is done within the strata.
Types
1. Proportionate Stratified Random Sample: The size of Each Strata is
proportionate to the population size of strata when looked at across the entire
population. This means that each stratum has the same sampling fraction
2. Disproportionate Stratified Random Sample: In this type the different
strata do not have the same sample fraction as each other.
For instance if four strata 200, 400, 600,800, people you may have choose
different sampling action for each stratum

Cluster Sampling
Cluster sampling is used when the population has "natural" but relatively
homogeneous (similar) groups. It is often used in market research surveys.
• The total population is divided into these groups (or clusters)
• A simple random sample of the groups is selected. Then
• The required information is collected from a simple random sample of the
elements within each selected group.

Systematic Sampling
• In systematic random sampling, the researcher first randomly picks the first
data item from the population. Then, the researcher will select each n'th data
item from the list.
• Two methods are used:
• From a population total of 100 data items 12 pieces of data are to be
selected. A starting number is picked first eg. 5. Then an interval is picked
eg. 8 so every eighth data item is selected to produce 5, 13, 21, 29, 37, 45,
53, 61, 69, 77, 85, 97.
• The sample size is identified eg 30. The total number of the population is
divided by the sample size to obtain the sampling fraction. This is used as
the constant difference between the data items.

Non Probability Sampling
• In any form of research, true random sampling is always difficult to achieve.
• Most researchers are bounded by time, money and workforce and because of
these limitations, it is almost impossible to randomly sample the entire
population.
• Non-probability sampling is where the samples are gathered in a process
that does not give all the individuals in the population equal chances of
being selected.
• The sample may or may not represent the entire population accurately.
Therefore, the results of the research cannot be used in generalizations about
the entire population.
Types of Non Probability sampling
• Convenience Sampling
• Consecutive Sampling/Sequential sampling
Judgmental/Purposive Sampling
 Convenience Sampling
The samples are selected because
• They are accessible to the researcher.
• They are easy to recruit.
They can be a self-selection of individuals willing to participate. (a self-selected
sample)

Consecutive Sampling/Sequential Sampling
Consecutive sampling Includes ALL accessible subjects that are available, as part
of the sample. The sampling schedule is completely dependent on the researcher
since a second group of samples can only be obtained after conducting the
experiment on the initial group of samples

• Judgmental/Purposive Sampling
 Convenience Sampling
The samples are selected because
• They are accessible to the researcher.
• They are easy to recruit.

• They can be a self-selection of individuals willing to participate. (a self-selected sample)
  
  Situation sampling

 When researchers are interested in events that happen infrequently, they rely 

on event sampling to sample behavior.

Why and in what situation sampling is inevitable ?

1. When population is infinite

2. When the items or units destroyed under investigation

3. When the results are required in a short time

4. When the resources for a survey are limited particularly money and trained 

persons

5. When area of survey is wide