How Can Samples Describe Populations?
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Introduction A facet of modern society is the vast amount of information and knowledge that is available, communicated and accumulated. Adding to this knowledge base through research necessitates a need to organise, simplify and summarise the information, if it is to be useful. The scientist's goal in general is to investigate and describe the implications of findings of a given problem or hypothesis. When phenomena are not of natural sciences but of a sociological character, there is debate on what serves as validation of hypothesis. It is therefore imperative for any investigation into social phenomena to consider the research methodologies used to explore subject matters and the ramifications that the subsequent results imply. Social sciences concentrate on the interaction of people and communities in relation to the infrastructure and environment that affects them. The main information seeking tools that are used in the field of study are surveys/questionnaires and interviews. The broad scope of social sciences means that the investigation could involve a very large, scaling down to a very few, number of subjects. This is obviously dependant upon the particular study. What is common to all cases though is the need for the collected data to be accurate through being representative and reflective of the total population under investigation. Representative refers to the fact that when investigating social phenomena, the data collected should mirror views of the whole population. Unless the investigation is focused on small populations, it is not usually possible to survey the entire population because there are often too many subjects. This leads the scientist into a dilemma; how is it possible to be completely representative in a survey without inclusion of the whole population?
The sample is then taken by surveying every member of the population of the randomly chosen clusters. It is therefore necessary to choose the clusters so the number of members in the clusters is low enough to allow every member of the cluster to be included in the study. In contrast to stratified random sampling, the divided clusters should be heterogeneous. They should ideally be a scaled down representation of the theoretical population. Random cluster sampling is used when there is clear logistical advantage, for example in say conducting data collection in a localised area. However, it does remain the fact that the clusters have to be representative of the theoretical population, if the method is to be effective.  One of the most important features of probability based sampling is that once the sample is chosen, the investigator has no subjective choice in who to include in the study, this has been predefined on a probability based permutation. Non-Probability Sampling Non-probability based sampling is a class of sampling that does not use the rigors of statistics and seeks other approaches to select a sample. The difference between non-probability and probability sampling is that former does not involve random selection.  Accidental/Haphazard/Convenience samplings are all the same form of sampling that simply determines the sample by what samples are available. Such methods include asking for volunteers and 'on the street' surveys. The problem with 'accidental' samples is that there is no way of ensuring that the samples are representative of the populations that will be generalised to in the analysis.
The two classes of sampling, probability and non-probability based, have been discussed and it is unambiguous that probability based methods offer advantages in the precision and representativeness that can be achieved. However, the use of quota sampling does not strictly imply that the data collected will be unrepresentative; it is simply more likely to be so. Therefore, care is needed when using quota sampling. Factors such as location, time (of survey) and modals of assumption need to be thoroughly thought through because such issues possible influence results. There is also support for multistage sampling methods; where a number of techniques (both probability and non probability based methods) are employed. Such methods are often used when the theoretical population is very large and several stages are used to define the sample.  The number of samples used is also a very important consideration in sampling. Certain sampling techniques will be more suitable than others and it could be expected that methods found to be highly successful in ethnographic studies may not be at all appropriate for operational research. It is the duty of the investigating party to ensure that the numbers of samples are sufficient and the sampling technique is suitable. Most important consideration should be given to the content and objective of the research given the context and scope in deciding such matters. Research methodology is always a compromise between options, and choices are frequently determined by the availability of resources . Therefore, when selecting a sampling technique it needs to be chosen on context and resource issues that are unique to the study. This will lead to representative data that could be analysed and concluded about with conviction.
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