Snowball sampling is a non-probability sampling method where new units are recruited by other units to form part of the sample. Snowball sampling can be a useful way to conduct research about people with specific traits who might otherwise be difficult to identify (e.g., people with a rare disease).
Also known as chain sampling or network sampling, snowball sampling begins with one or more study participants. It then continues on the basis of referrals from those participants. This process continues until you reach the desired sample, or a saturation point.
Example: Snowball samplingYou are studying changes over time in romantic relationships. You are relatively new to the city, so you are finding it difficult to find participants yourself. You first look for interviewees within your own circle of friends and acquaintances.
A number of criteria are used for the selection:
The couple must have been together for a period of at least five years.
The couple must live together now.
The couple must live within a certain geographic area.
The couple must have examples of changes or challenges they have experienced together (e.g., long-distance, illness or loss of a loved one).
After the first interview with a couple from your own circle, you ask them if they know of another couple who may be interested in taking part in the research. Once you have interviewed the second couple, you ask again for a referral. You continue until you have conducted interviews with 30 couples, which is your desired sample size.
Quota sampling is a non-probability sampling method that relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.
You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata.
The aim of quota sampling is to control what or who makes up your sample. Your design may:
Replicate the true composition of the population of interest
Include equal numbers of different types of respondents
Over-sample a particular type of respondent, even if population proportions differ
Example: Quota sampling Suppose you want to gauge consumer interest in a new meal kit delivery service in Hull.
Depending on your research goals, you can divide your population into several strata, such as:
Dietary preferences
Age group
Postal code
Let’s say you want to focus on dietary preferences. You divide the population into meat eaters, vegetarians, and vegans, drawing a sample of 600 people. Since the company wants to cater to all consumers, you set a quota of 200 people for each dietary group. In this way, all dietary preferences are equally represented in your research, and you can easily compare these groups.
You continue recruiting until you reach the quota of 200 participants for each subgroup.
Purposive sampling refers to a group of non-probability sampling techniques in which units are selected because they have characteristics that you need in your sample. In other words, units are selected ‘on purpose’ in purposive sampling.
Also called judgemental sampling, this sampling method relies on the researcher’s judgement when identifying and selecting the individuals, cases, or events that can provide the best information to achieve the study’s objectives.
Purposive sampling is common in qualitative research and mixed methods research. It is particularly useful if you need to find information-rich cases or make the most out of limited resources.
Convenience sampling is a non-probability sampling method where units are selected for inclusion in the sample because they are the easiest for the researcher to access.
This can be due to geographical proximity, availability at a given time, or willingness to participate in the research. Sometimes called accidental sampling, convenience sampling is a type of non-random sampling.
Example: Convenience samplingSuppose you are researching public perception towards the city of Seattle. You have determined that a sample of 100 people is sufficient to answer your research question.
To collect your data, you stand at a subway station and approach passersby, asking them whether they want to participate in your research. You continue to ask until the sample size is reached.
Note: Make sure not to confuse random selection with stopping passersby at random.
In probability (or random) sampling, random selection means that each unit has an equal chance of being selected.
In convenience sampling, stopping people at random means that not everyone has an equal chance of forming part of your sample. For instance, here you have excluded people who did not pass through that subway station on the day and time you were collecting your data.
Non-probability sampling is a sampling method that uses non-random criteria like the availability, geographical proximity, or expert knowledge of the individuals you want to research in order to answer a research question.
Non-probability sampling is used when the population parameters are either unknown or not possible to individually identify. For example, visitors to a website that doesn’t require users to create an account could form part of a non-probability sample.
In non-probability sampling, each unit in your target population does not have an equal chance of being included. Here, you can form your sample using other considerations, such as convenience or a particular characteristic.
In probability sampling, each unit in your target population must have an equal chance of selection. This minimises the risk of selection bias.
Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling.
To qualify as being random, each research unit (e.g., person, business, or organisation in your population) must have an equal chance of being selected. This is usually done through a random selection process, like a drawing, to minimise the risk of selection bias.
TipBe sure to name or number your target population to ensure accurate randomisation (random assignment).
Social desirability bias occurs when respondents give answers to questions that they believe will make them look good to others, concealing their true opinions or experiences. This type of research bias often affects studies that focus on sensitive or personal topics, such as politics, drug use, or sexual behaviour.
Social desirability bias is a type of response bias. Here, study participants have a tendency to answer questions in such a way as to present themselves in socially acceptable terms, or in an attempt to gain the approval of others.
It is especially likely to occur in self-report questionnaires, but it can also affect the validity of any type of behavioural research, particularly if the participants know they’re being observed.
The risk of researchers or respondents influencing (biasing) a study and its results, whether consciously or unconsciously, is inherent to conducting human-centred research. However, there are ways to detect and reduce bias in your research design if you know what to look for.
Example: Social desirability biasYou are conducting a study about the relationship between gambling and drug use. You ask participants to fill out a survey, where you ask about their experiences and habits regarding the use of cocaine and casino gambling. These types of questions require participants to admit to attitudes, beliefs, or behaviours that may violate social norms.
Due to this, participants may downplay how often they visit a casino or use cocaine. In other words, they may give answers they consider to be socially desirable in order to project a favourable image of themselves, or to avoid being perceived negatively.
