56 Nonprobability sampling Quantitative methods Sampling UvA

probability sampling can be contrasted with nonprobability sampling and non-probability sampling some elements in the sampling frame either have zero probability to be selected or their probability is unknown as a consequence we cannot accurately determine the margin of error it's also impossible to determine the likelihood that a sample is representative of the population there are several types of nonprobability sampling I'll discuss the four most common types convenient sampling snowball sampling purpose of sampling and quota sampling convenience sampling or accidental sampling is the simplest form of nonprobability sampling in convenience sampling elements are selected that are the most convenient the most easily accessible for example if I'm interested in investigating the effectiveness of online lectures on study performance I can recruit students in courses that I teach myself of course this is a highly selective sample of students from a particular University in a particular bachelor program results will almost certainly be influenced by specific characteristics of this group and might very well fail to generalize to all university students in my country let alone students in other countries so the risk of bias is high we have no way to determine how closely the sample value is likely to approach the population value even so convenient samples are used very often because sometimes it's simply impossible to obtain a sampling frame in other cases the effort and expense necessary to obtain a sampling frame are just not worth it for example when a universalistic causal hypothesis is investigated snowball sampling is a specific type of convenient sampling in snowball sampling initially a small group of participants is recruited the sample is extended by asking the initial participants to provide contact information for possible new participants these new participants are also asked to supply contacts if all participants refer new ones the small sample can grow large very quickly suppose we want to sample patients who suffer from a rare type of cancer we could approach a patient interest group for example and ask the initial participants if they can put us in contact with other patients that they know through other interest groups or through their hospital visits we continue to ask new participants to refer others to us until the required sample size is reached snowball sampling is very useful for hard-to-reach close community populations of course all disadvantages of convenient sampling also applies to snowball sampling maybe even more so because there's the added risk that we're selecting a clique of friends colleagues or acquaintances these people could share are characteristics that differ systematically from others in the population in purpose of sampling elements are specifically chosen based on the judgment of the researcher a purpose of sample can consist of elements that are judged to be typical for the population so that only a few elements are needed to estimate the population value a purpose of sample can consist of only extreme elements for example to get an idea of the effectiveness of social workers working with extremely uncooperative problem families elements can also be purposively chosen because they're very much alike or reversely very different for example to get an idea of the range of values in the population or elements can consist of people who are judged to be experts for example when research concerns opinions on matters that require special knowledge purpose of sampling is used mostly in qualitative research so I won't go into further details here suffice it to say that purposive sampling suffers all the sameness advantages that convenience sampling does the researchers judgments can even form an additional source of bias quota sampling is superficially similar to stratified random sampling participants in the sample are distinguished according to characteristics such as gender age ethnicity or educational level the relative size of each category the population is obtained from a National Statistics Institute for example this information is used to calculate how many participants are needed in each category so that the relative category size is in the sample correspond to the category sizes in the population but instead of randomly selecting elements from each stratum participants for each category are selected using convenience sampling elements are sampled until the quotas in all categories are met although this approach might seem to result in a representative sample all kinds of biases could be present suppose the choice of participants is left to an interviewer then it's possible that only people who seem friendly and cooperative are selected if a study uses nonprobability sampling the results should always be interpreted with great caution and generalized only with very great reservation