Research Design - Data - Data Sampling
3 important questions on Research Design - Data - Data Sampling
What is a sample and why do you take a sample when conducting research?
A sample is a smaller group (sample) we take out of a larger group (the population) to make claims about the larger group. Remember the sample population can provide a wide range of data:
We take samples because collecting and analysing all available evidence when conducting research is not always possible (e.g. Surveying every adult in England). Adults in England= population; the group of cases from which you want to collect data.
We take samples because collecting and analysing all available evidence when conducting research is not always possible (e.g. Surveying every adult in England). Adults in England= population; the group of cases from which you want to collect data.
What are generalizations and for which researchers is this important?
Generalizations are inferences about the behavior of the population one makes from studying the sample (usually statistical for a social scientist). In other words: it is extending our findings, results or conclusions from the sample to population.
The purpose of sampling is to create an objective sample that best represents the population, so that one can make generalizations about the population from the sample.
The purpose of sampling is to create an objective sample that best represents the population, so that one can make generalizations about the population from the sample.
What are the 4 types of nonrandom sampling procedures? And when do you use nonrandom sampling?
1. Convenience sampling
2. Snowball sampling
3. Purposive sampling
4. Quota sampling
You can use nonrandom sampling for a number of reasons
2. Snowball sampling
3. Purposive sampling
4. Quota sampling
You can use nonrandom sampling for a number of reasons
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