How does cost play a factor in whether researchers use a sample or the total population? There are several types of random sampling. What are the different types of random sampling? When is it best to use what kind of study or population? How does the researcher decide which type of randomization to use? One example is a convenience sample. When do you think researchers use this? The most "famous" convenience sample is psych 100 college students, especially on large campuses. Why would they be a convenience sample? What population could you generalize to if you use a class of psych 100 students as your sample? Could they extend beyond their own "group"? What considerations would you have to make if you used this kind of class for your sample and wanted to then generalize to everyone else?
What is the goal of using the sample? What are researchers trying to do?
Cost is one of the most important factors when dealing with sample vs population.
For every person you include in your research, you must think of the costs:
- cost of recruiting the person to participate
- time to administer the questionnaire if done face-to-face; cost of hosting the server if done on-line
- if your give an indemnity to the person for their participation
- data entry of their results
- analysis of their data
So this could really add up. Therefore you need to think of your budget - if you budget is a certain value you might not have enough resources to poll every single person in your population. The cost-effective way would be to take a random sample that is large enough to represent the population, and work with these individuals.
Different types of random sample:
simple: here you have a pool of people, and you just randomly select people from the pool to be in your sample
Stratified sampling: if you have sub-samples within the population, you would make sure to select individuals from each ...
This posting explains how cost can influence your decision wether to use a sample or the entire population. It gives a cost-effective solution in making sure that your research is sound when dealing with samples.