Sampling for Your Ph.D. Research
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Sampling for Ph.D. Research
In research, a sample is a group of people, objects, or items taken from a larger population for measurement. To ensure the findings can be generalized to the entire population, the sample should be representative. The selection of samples depends on factors such as the nature of the study, the size of the population, the sample size, the degree of precision required, and available resources. Sampling is crucial because surveying the entire population is often impractical, budget constraints can prevent a full survey, and time constraints necessitate quick results.
Social science research typically employs two broad approaches to sampling:
Probability Sampling: All elements (e.g., persons, households) in the population have some chance of being included in the sample, and the probability of selection can be calculated.
Non-Probability Sampling: Population elements are selected based on their availability (e.g., volunteers) or the researcher’s judgment of their representativeness. As a result, a segment of the population remains unidentified and is not included (for example, individuals who did not volunteer).
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At Thesis PhD, our experts assist you in selecting the appropriate sampling method based on your research objectives, chosen research design, data collection method, and target population. Whether you need to conduct probability or non-probability sampling, we help you make the right choice.
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Sampling Techniques in Thesis PhD:
Various sampling techniques are available for consideration:
Random Sampling:
This method offers equal chances to each respondent, making it straightforward in principle. However, defining the exact sample size becomes challenging when dealing with a large target population (Saunders et al., 2003).
Systematic Sampling:
This method, commonly known as the Nth name selection method, entails choosing every Nth record from the target population (Saunders, 2003).
Stratified Sampling:
This approach involves classifying the research population based on specific characteristics, after which participants are selected accordingly (Saunders, 2003).
Convenience Sampling:
Here, researchers select a subset of participants due to time and cost constraints, assuming that this subset adequately represents the target population. Typically, subjects with easy accessibility are chosen (Saunders, 2003).
Quota Sampling:
Commonly used in face-to-face interviews, this method involves setting quotas based on characteristics like age and gender to ensure the final sample is representative. However, the sample’s accuracy cannot be estimated as it’s non-random.
Snowball Sampling:
This method is useful for reaching difficult-to-access groups such as unemployed individuals or minority ethnic residents. It starts with contacting a few potential respondents who then refer others with similar characteristics.
Self-Selection:
Respondents themselves decide whether to participate in the survey or not, making this technique self-explanatory.
Judgment sampling:
In this method, researchers use their judgment to select a subset of participants, often for evaluating technical aspects in sampling techniques. Selected participants are strategically positioned to yield better solutions (Saunders, 2003).
Ensuring the chosen sample represents the statistically significant population requires measuring the population size accurately. Sampling involves selecting a portion of the target population to act as a representative sample.
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