Sample Design and Sample Population

Sample Population
A population sample is a smaller population taken from the larger population to represent the larger population. The views of the smaller population are assumed to be representing the views of the larger population that it was extracted from.

Sampling
This is the process of coming up with the sample population. It begins by the researcher first defining the target population in terms of: elements. sampling units. extent. time. 

Then determining a sampling frame. A sampling frame is a representation of the elements of the target population. It consists of a list of directions for identifying the target population. At this stage, it is important to recognize any sampling frame errors that may exist. The next steps involve selecting a sampling technique and determining the sample size. In addition to quantitative analysis, several qualitative considerations should be taken into account in determining the sample size. Finally, execution of the sampling process requires detailed specifications for each step in the sampling process.

There are two main categories of sampling procedures:
 * 1) Random / Probability.
 * 2) Non Random.

Random / Probability Methods
When using these methods, there is an equal chance for each individual to be selected. Probability Sampling Methods:
 * 1)   Simple random sample: a sample is drawn randomly from a list of individuals in a population. For example, from a group of 100 people, you write numbers on paper from 1 to 100 with red and blue color. Then each person who has a number with red color is selected.
 * 2)    Systematic selection procedure sample: a variant of a simple random sample in which a random number is chosen to select the first individual and so on from there. For example you can choose to pick every 10th person starting from the 9th Person. e.g. 9   19   29   39   49   59
 * 3)    Stratified sample: dividing the total population into smaller, manageable groups, and randomly sampling from each group. For example you may divide the population into gender and sample from each gender or you may divide the population according to age or title.
 * 4)    Cluster sample: dividing up a population into smaller groups, and then only sampling from one of the groups. Cluster sampling is " according to Lee, Forthofer, and Lorimer (1989), is considered a more practical approach to surveys because it samples by groups or clusters of elements rather than by individual elements" (p. 12). It also reduces interview costs. However, Weisberg et. al (1989) said accuracy declines when using this sampling method. Example of cluster sampling is when performing research on only women.
 * 5)    Multistage sampling: first, sampling a set of geographic areas. Then, sampling a subset of areas within those areas, and so on. For example When conducting an educational research in a country you divide the country into regions, pick a few regions and sub-divide them into districts or Divisions or Locations e.t.c

Non-Random Sampling
These methods rely primarily on the researchers judgement. Types of non-probability sampling include:
 * 1) Convenience sampling: The researcher chooses the sample based on convenience (because they are convenient). It is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. This non-probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.
 * 2) Judgment sampling: is a common non-probability method. The researcher selects the sample based on judgment. This is usually and extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.
 * 3) Quota sampling: is the non-probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratus and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratus are filled by random sampling.
 * 4) Snowball sampling: is a special non-probability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population. For example, if a census indicates that more than half of the population is female, then the sample will be adjusted accordingly.