Selecting a Sample in a research process
The accuracy of your estimates largely rest on the way you select your sample. The basic objective of any sampling design is to minimise the gap between the values obtained from your sample and those prevalent or dominant in the population.
The underlying theory in sampling is that, if a relatively small number of units is scientifically selected, it can provide a fairly true reflection of the sampling population being studied.
Sampling theory is guided by two principles:
- Avoidance of bias in selecting sample.
- the attainment of maximum precision for a given outlay of resources.
There are three categories of sampling design:
- Random sampling designs.
- Non random sampling designs.
- Mixed sampling designs.
There are many sampling strategies within the first two categories. You need to be equipped with these sampling designs to select the one most appropriate for your study. You need to know the strength and limitations of each. You also need to know the situations in which it can or it cannot be applied in order to select the most appropriate design for your research process. The type of sampling strategy you use also determines your ability to generalise from the sample to the total population and the type of statistical tests you can perform on the data.