Understanding Population, Sampling, and Research Methods
Understanding Population and Sampling in Research
Population: The entire group (of individuals or objects) that we want to know something about.
Population of Interest: A group in which the researcher has an interest in drawing conclusions from the study. Examples include ‘the population of Asia’ or ‘the population of Singapore’.
Population Parameter: A numerical fact about a population. These are constants.
Sample: A proportion of the population selected for the study. This is preferred over a census when data is not readily available. A sample is less costly administratively, and data collection and processing are faster.
Census: An attempt to reach out to the entire population of interest. Note that a 100% response rate might not be achieved.
Estimate: Inference about the population’s parameter, based on information obtained from a sample.
Sampling Frame: ‘Source material’ from which the sample is drawn. It may not cover the population of interest or may contain units that are not in the population of interest.
Note: To fulfill generalisability criteria, the sampling frame should be equal to or larger than the target population. If the sampling frame fails to cover any member of the target population, it cannot be used to generalize fully to the target population as there exist members that have been left out.
Types of Research Questions
- Making an estimate about the population:
- What is the average number of hours that students study each week?
- What proportion of all Singapore students is enrolled in a university?
- Test a claim about the population:
- Does the majority of students qualify for student loans?
- Is the average course load for a university student greater than 20 units?
- Compare two sub-populations/Investigate a relationship between two variables in the population:
- In university X, do female students have a higher GPA score than male students?
- Does drinking coffee help students pass the math exam?
Sampling Methods
Probability Sampling: The selection process is via a known/randomized mechanism in which every unit in the population has a non-zero and known probability of being selected. This eliminates biases associated with human selection.
1. Simple Random Sampling
Units are selected randomly from the sampling frame by a random number generator. Sample results do not change haphazardly from sample to sample, and variability is due to chance.
Advantages
- Sample tends to be a good representation of the population.
Disadvantages
- Subject to non-response (individuals choose to opt out of the study).
- Possible limited access to information as the selected individuals may be located in different geographical locations.
2. Systematic Sampling
A method of selecting units from a list through the application of a selection interval K, so that every Kth unit on the list, following a random start, is included in the sample.
Advantages
- More straightforward and simpler selection process than simple random sampling.
- No need to know the exact population size at the planning stage.
Disadvantages
- May not be representative of the population if the sampling list is non-random.
3. Stratified Sampling
The population is broken down into strata in which each stratum is similar in nature, but size may vary across strata. A simple random sample is then employed from every stratum.
Advantages
- Able to get a representative sample from every stratum.
Disadvantages
- Quite complicated and time-consuming.
- Need information about the sampling frame and stratum, which can be hard to define.
4. Cluster Sampling
Breaking down the population into clusters, then randomly sample a fixed number of clusters. Proceed to include all observations from the selected clusters.
Advantages
- Less tedious, costly, and time-consuming as opposed to other sampling methods (e.g., stratified sampling).
Disadvantages
- High variability due to dissimilar clusters or small numbers of clusters.