The terms reproducibility, repeatability, and replicability are sometimes used interchangeably, but they mean different things.
A research study is reproducible when the existing data is reanalysed using the same research methods and yields the same results. This shows that the analysis was conducted fairly and correctly.
A research study is replicable (or repeatable) when the entire research process is conducted again, using the same methods but new data, and still yields the same results. This shows that the results of the original study are reliable.
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.
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
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 judgmental sampling, this sampling method relies on the researcher’s judgment when identifying and selecting the individuals, cases, or events that can provide the best information to achieve the study’s objectives.
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.
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.
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 organization in your population) must have an equal chance of being selected. This is usually done through a random selection process, like a drawing.
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. It often affects studies that focus on sensitive or personal topics, such as politics, drug use, or sexual behavior.
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 in self-report questionnaires, but it can also affect the validity of any type of behavioral research, particularly if the participants know they’re being observed. However, there are ways to detect and reduce research bias in your research design if you know what to look for.