Whenever you try to conduct any research about any group of people then it is rarely possible that you will collect data from each and every person in that group. Instead of that you will try to select a specific sample. The sample of the group of individuals will help n participating in the research.
For drawing a valid conclusion from the results, you have to decide very carefully that how you will select the representative of that group as a whole. There will be two type of sampling methods and they are provided in the following:
- Probability sampling – This sampling involves random selection which will allow you to make statistical inferences which will be about the whole group
- Non-probability sampling – This sampling method involves non-random selection which will be based on the convenience or on other criteria which will allow you to easily collect initial data only.
It is important that you clearly explain why you have selected that specific type of sampling method in the methodology section of the research paper or the thesis
Population vs sample
The first thing which you need to understand is the difference between a population and a sample and then identify the targeted population of your research paper.
The population is known to be the entire group which you want to develop a conclusion about and a sample is known as the specific group of the individual which you will collect data from. The population can also be defined in terms of the geographical location, income, age and many other types of characteristics.
This topic can be very brad as well as very narrow. When you want to make an inference about the whole adult population of the country or may be the research focuses on the consumers of the certain company, patients along with a specific health condition, or it maybe a student in a single school.
It is very important that you define carefully your targeted population as per the purpose and the practicalities of the research paper. If the population is large, demographically mixed and also geographically dispersed then it might become much difficult for gaining access into a representative sample.
Sampling frame is known as the actual list of the individual’s form where the whole sample will be drawn. Ideally it must involve the entire targeted population and nobody who is not a part of that population.
For example, suppose you are doing a research over a working conditions at company X. You select a population of 1000 people and all of them are employees of that company. Your sampling frame is company’s HR database which contains the lists of the names along with their contact details for every employee.
The number of the individuals you selected for you sample will depend over the size of population and also on how precisely you develop the result for representing the whole population as a whole. Here you can also use a sample size calculator for determining how big you selected sample should be. In general, the accuracy as well as the confidence of your research paper will be dependent over the size of the whole population selected for sample, if it is large then it will provide better and accurate results.
Probability sampling methods
Probability sampling method states that every member of the population has an equal chance for getting selected. This sampling methods mainly gets used in the quantitative research. In case you want to develop result which are representative of the total population then you need to utilize the probability sampling technique.
There are basically four types of probability samples and they are provided following:
- Simple random sampling – In this type of sampling each of the members of the whole population has equal chance of getting selected. Here the sampling frame would involve the whole population only. For conducting this type of sampling you can utilize the tool like random number generators or any of the other techniques which are based totally on chance.
- Systematic sampling – This type of sampling is also very similar to simple random sampling however this is usually a slightly easier for conducted by the researcher. Each of the member of the whole population gets listed with the help of a number however instead of randomly generating the numbers, individuals are chosen at a regular intervals.
If this technique is chosen then it is very important that you ensure there is no hidden pattern in this list which might skew the whole sample. For example, if HR database groups the employees by a team, and the team members are listed in order of their seniority.
Then there will be a lot of risk that the interval might skip over many people in the junior roles which will ultimately result into a sample which is skewed only towards the senior employees.
- Stratified sampling – This type of sampling method is very appropriate when the population has a mixed characteristics and along with that you want to ensure that each of the characteristics is very proportional with the sample.
Here you will divide the whole population into subgroups which is known as strata and it will be based on relevant characteristics like gender, age group, job role, and income bracket. From the whole proportion of the population you can also calculate to understand how many people need to be involved into a subgroup of sample.
- Cluster sampling – This type of sampling method involves dividing the population subgroups. However, each of the subgroup should consist same characteristics to whole sample. Apart from sampling only one individual from the subgroup you can randomly pick the entre subgroups.
If this is practically possible then you can involve every individual from each of the sampled cluster. If the cluster is large then you can simple sample the individuals from each of the cluster using one of the techniques stated above. This method is much better for dealing with large populations.
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