Convenience sampling
Also known as opportunity sampling, where participants are selected based on their accessibility and availability to the researcher, ie. proximity.
| Strengths | Weaknesses |
| – Quick, easy to implement – Cost-effective – Useful for exploratory studies or when access to participants is limited | – Prone to selection bias as participants are chosen based on availability – Results may not be generalizable to the broader population |
Quota sampling
Based on a quota of what sort of sample and the characteristics they must have, recruiters recruit participants until the quotas are met.
| Strengths | Weaknesses |
| – Researchers can control demographic characteristics by setting quotas for specific groups, such as age, gender, socioeconomic status | – Results may not accurately reflect the population if quotas are not set appropriately – Requires careful monitoring to ensure quotas are met without compromising randomness |
Random Sampling
Start by defining the population you want to study. For example, if you’re investigating the effects of social media on teenagers’ mental health, your population would be teenagers. After which, craft a sampling frame, ie. list all the individuals in your population and employ a random selection method to choose your sample. This could involve assigning each member of the population a number and then using a random number generator to select participants, or using a table of random numbers to pick individuals.
| Strengths | Weaknesses |
| – Results are more likely to be statistically valid and reliable- Provides the highest level of representativeness and generalizability – Ideal approach for a representative sample, every member of the target population has an equal chance of becoming part of the sample | – Not always possible for practical reasons, ie. if target population is large, for example, all teenagers in the world, you cannot ensure that each member of this population gets an equal chance to enter your sample – May not be feasible in certain situations, such as when the population is highly dispersed- Requires a complete list of the population, which may not always be available |
Self-selected Sampling
Done by advertising, maybe on a notice board and sampling those who responded, also known as voluntary sampling.
| Strengths | Weaknesses |
| – Participants are likely to be highly motivated and willing to participate | – Prone to volunteer bias, as participants self-select based on their interest or characteristics – Results may not be generalizable to the broader population. – Difficult to control for extraneous variables that may influence participant self-selection |
Snowball Sampling
When existing study participants recruit additional participants from their social network.
| Strengths | Weaknesses |
| – Effective for studying hard-to-reach or hidden populations – Allows researchers to access participants through existing social networks – Can yield rich and diverse data, especially in qualitative research | – May lead to oversampling of certain groups within the network |
Stratified Sampling
Researchers divide subjects into subgroups called strata based on characteristics that they share (ie. race, gender, age). Once divided, the next step is to determine the proportion of participants (ie. 20%) to be sampled from each subgroup. This proportion is typically based on the relative size or importance of each subgroup within the population. Participants are then randomly selected from the subgroups.
| Strengths | Weaknesses |
| – Similar to weighted average, captures key population characteristics in the sample – Ensures representation of specific subgroups within the population | – Requires accurate information about the population’s characteristics to properly define strata – Can be complex and time-consuming to implement, especially with large populations or numerous strata |
Systematic Sampling
Researchers first determine the sampling interval, ie. every 5th person, and then randomly select a starting point within the population. This initial random selection ensures that the sampling process remains unbiased and representative. For instance, if the sampling interval is determined to be 5 and the randomly chosen starting point is student number 3, researchers would select every 5th student on the list thereafter.
| Strengths | Weaknesses |
| – Can be more representative than convenience sampling while being less resource-intensive than random sampling | – Requires a complete list of the population and careful selection of the sampling interval – May not be suitable for populations with irregular distributions or clustering |
Detailed Notes on Research Methods