Types of sampling and ways of selecting respondent in a sample size

QUESTION
Population and sampling are very important when dealing with research. With vivid examples, discuss types of sampling and ways of selecting respondents in a given sample size.
Sampling is the process of selecting small number of people, object or units (sample) to represent the entire population.  Example in checking the soil type it may be taken small amount of soil, the amount soil taken is said to be sample. Sampling is very important in a field research because it can save time since can produce result from within short and relative time, it is cheap in terms of cost, provide a greatest scope in terms of variable that may be studied and it is scientifically and statistically justifiable. Sampling has divided into two types which are probability and non probability (Mzezele & Kibuuka 2012). By starting with probability, the following are the explanations:-
Probability sampling, this is the sampling method where selection of sample is done randomly by chance. Here each individual in a population has an equal chance of being included in the sample.

The following are the ways of selecting respondents in a given sample size within a probability sampling (According to Adam & Kamuzora 2008).

Simple random sampling, this gives every possible combination element in a population equal chance of being included in the sample. It is chance sampling technique where each of every items or individual in the population has an equal opportunity in the inclusion the sample. Simple random sampling has some methods which are used when selecting a sample which are; by lottery method, by the table of random number and computer method which is similar to table random number but under this number are packed by computer.
Systematic sampling, this is the method or type of sampling by selecting of individual or items on a given regular interval. It is very applicable when population sample is large and the target population is evenly distributed. For example, formula of sampling interval K = N/n. where N is the total number of a population, n is a sample size and K is the sampling interval.
Example, if the total population is 1000 individual and the numbers of people who must be in the sample are 100. What will be the sampling interval?
Steps    
Calculate sampling interval or fraction  K=    K=  = 10
Picking the starting point by any method of simple random sampling.
Picking systematically with the specific interval (k) by adding (k) to the pick number until the required single is achieved.

Stratified sampling, is the method of probability sampling involving a selection of a study sample by dividing the targeted population into the homogenous sub group (strata) and then simple random sampling in each sub group. Suppose the target population consist of 700 male sub population and 300 female sub population, create male and female sub sample provided that 100 total population sample is required.
Calculate the total number of the population   700m + 200l = 1000
Finding the sampling ratio. Given by K =  =  =  ratio.
Calculate sub sample of male and female by taking sampling ratio multiple by sample population of each. Male = 1/10 × 700 = 70 male. Female = 1/10 ÷ 300= 30 female.
Cluster or spatial sampling is the type of probability sampling used when target population is displaced over a wide geographical area. Under this the total area and study is divided. The total into a number at a smaller non-overlapping areas, generally called geographical cluster. Then a number of smaller areas are randomly selected. All units in these small areas are included in the sample.

Multi stage sampling, this is the further development of the idea cluster sampling. This technique is meant for big inquiries extending to a considerably large geographical area like an entire country. Multi stage sampling can be a complex form of cluster sampling because it is the type of sampling which involve dividing the population into groups (or cluster). Then one or more clusters are chosen at random and everyone within the chosen cluster is sampled. Using the entire sample in the selected clusters may be prohibitively expensive or unnecessary. Under these circumstances, multistage cluster sampling becomes useful.

Non probability sampling is the type of sampling which does give each items or element in a target population equal chance to be included in sampling. It may also be defined as a group of sampling techniques that help researchers to select unit from a population that they are interested in studying. The following are the ways used in selecting respondent in a sample size within a non probability sampling (According to Njiku William 2007).

Accidental sampling, under this method, the researcher collect data from a respondent for a given research study he or she meets accidentally during the period of research. With this accidentally or convenience sampling, the samples are selected because they are accessible to the researcher. This way is considered easiest, cheapest and least time consuming.

Snow ball sampling, this way is used when there is a very small population size. In this way of sampling, the researchers ask the initial subject to identify another potential subject who also meets the criteria of the research. The downside of using the snow ball sample is that it is hardly representative of the population.

Quota sampling is a non probability sampling technique where in the researcher insures equal or proportionate representation of subjects depending on which trait is considered as basis of the quota. For example, if basis of the quota is college year level and the researcher needs equal representation, with a sample size of 100, he must select 25 1st year students, another 25 2nd year students, 25 3rd year and 25 4th year students. The bases of the quota are usually age, gender, education, race, religion and socioeconomic status.

Judgment sampling is the type of non probability sampling where researcher purposeful chooses responder who’s in his or her own opinion thought are to be relevant of the study. It is more commonly known as purposive sampling. In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind. The researcher believes that some subjects are fit for the research compared to other individuals. This is the reason why they are purposely chosen as subjects.

Conclusion, sampling in research is of great vital since is among of things which make the research to be reached at its goal. The complete research have several process which are to be followed so as to have a right to speak on something, to evaluate, to make conclusion of a certain phenomena and properly justification on the research made through sampling.







REFERENCE
Adam, J. & Kamuzora, F. (2008). Research Methods for Business and Social Studies. Morogoro Tanzania. Mzumbe Book Project.
Mzezele, S. & Kibuuka, P. (2012). Geography in Focus. , Dar es Salaam. Oxford University Press (T) Ltd.
Njiku, J. W. (2007). The Essential of Practical Geography, Advanced Level. Dar es Salaam. Dar Printers Limited.
Zisti Kamili (2010). Practical Geography Alive. Dar es Salaam. Kot Publishers.