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The Comprehensive Guide to Sampling Techniques in Social Science Research
One of the most common reasons project students face "grilling" during a defense is a weak justification for their sample size. Sampling is not just about picking a group of people; it is about statistical representation.
### Understanding the Divide: Probability vs. Non-Probability
Probability sampling (like Simple Random or Stratified sampling) is the gold standard for quantitative research. It ensures that every member of your population has an equal chance of being selected, which significantly reduces "Researcher Bias." If you are writing a thesis that aims to generalize findings to a whole state or country, you must use this approach.
On the other hand, Non-Probability sampling (like Purposive or Snowball sampling) is often misunderstood as "lazy." In reality, it is essential for qualitative studies where you need to speak to specific experts or "hard-to-reach" groups. If your research is about the experiences of female CEOs in the oil sector, a random sample is useless—you need purposive sampling to find those specific individuals.
### The Taro Yamane Formula
For many Nigerian students, the Taro Yamane formula is the go-to for determining sample size from a finite population. But here is the secret: supervisors love it when you explain *why* you chose your margin of error (usually 5%). At uniSupport, we don’t just give you a number; we help you defend the logic behind that number. Remember, a sample that is too small lacks power, but a sample that is too large is a waste of your limited student resources.
### Understanding the Divide: Probability vs. Non-Probability
Probability sampling (like Simple Random or Stratified sampling) is the gold standard for quantitative research. It ensures that every member of your population has an equal chance of being selected, which significantly reduces "Researcher Bias." If you are writing a thesis that aims to generalize findings to a whole state or country, you must use this approach.
On the other hand, Non-Probability sampling (like Purposive or Snowball sampling) is often misunderstood as "lazy." In reality, it is essential for qualitative studies where you need to speak to specific experts or "hard-to-reach" groups. If your research is about the experiences of female CEOs in the oil sector, a random sample is useless—you need purposive sampling to find those specific individuals.
### The Taro Yamane Formula
For many Nigerian students, the Taro Yamane formula is the go-to for determining sample size from a finite population. But here is the secret: supervisors love it when you explain *why* you chose your margin of error (usually 5%). At uniSupport, we don’t just give you a number; we help you defend the logic behind that number. Remember, a sample that is too small lacks power, but a sample that is too large is a waste of your limited student resources.