Population and Sampling Methods in Research

A representative sample is an adequate reflection of the total group. If your sample is skewed—for example, if you only survey wealthy students to represent all students—your results will suffer from sample bias, meaning they do not accurately reflect the diversity of the population.

Three critical factors influence whether your sample is truly representative:

1. Sampling procedure: The method you use to select participants.
2. Sample size: If your sample is too small, the units likely will not be good representatives of the whole.
3. Participation rate: This refers to whether everyone selected for the sample actually participates. If certain types of people refuse to participate, your data becomes biased.

Exam Tip: When evaluating a study, don't just look at the sample size. Always ask about the participation rate. A large sample where only one specific type of person agreed to answer is less representative than a smaller, more diverse sample with high participation.

Non-Probability Sampling

Non-probability sampling methods are often used when researchers need to reach specific types of people or when a complete sampling frame is unavailable. In these methods, not every member of the population has a known or equal chance of being selected.

There are five common types of non-probability sampling:

• Convenience sample: Selecting participants who are easiest to reach, such as people standing in a specific shopping mall.
• Snowball sample: Asking initial participants to refer their friends or acquaintances to the study, which is useful for reaching hard-to-access groups via email or social media.
• Purposive sample: Selecting respondents because they possess particular characteristics relevant to the study, such as targeting only fans of a specific TV show.
• Quota sample: Setting a target number of respondents for specific subgroups (e.g., 50 men and 50 women) and continuing to recruit until those targets are met.
• Volunteer sample: A method where the researcher lets the participants come to them.

Probability Sampling

If your research goal is to achieve high representativeness and perform statistical generalisation, you should use probability sampling. In these methods, every individual in the population has a known chance of being elected for the sample.

Common probability sampling techniques include:

1. Simple random sampling: Defining the population and a sampling frame, then using a random process to select subjects.
2. Systematic random sampling: Selecting subjects based on a fixed system or interval (for example, choosing every 10th person on a list, or using a pattern like 1, 3, 5...).
3. Stratified random sampling: Dividing the population into subgroups (strata) and then randomly sampling from each subgroup to ensure all groups are represented.
4. Cluster random sampling: Dividing the population into "clusters" (often based on geography) and randomly selecting entire clusters to study.
5. Multi-stage cluster sampling: A more complex version of cluster sampling where you select clusters, and then randomly select individuals within those selected clusters.

Summary Table: Choosing Your Method

By carefully choosing between these methods based on your research question, you ensure that your findings are both meaningful and scientifically sound.