A researcher begins with specific characteristics in mind that they wish to examine and then they seek out research participants who cover that full range of characteristics. For example, if you are studying mental health supports on your campus, you want to be sure to include not only students, but also mental health practitioners and student affairs administrators.
You might also select students who currently use mental health supports, those who dropped out of supports, and those who are waiting to receive supports. Note that these are different than inclusion criteria, which are more general requirements a person must possess to be a part of your sample. For example, I might recruit Jane for my study because they stopped seeking supports this month, or I might recruit JD because they have worked at the center for many years.
Also, it is important to recognize that purposive sampling requires the researcher to have information about the participants prior to recruitment.
In other words, you need to know their perspectives or experiences before you know whether you want them in your sample. In this case, a purposive sample might gather clinicians, current patients, administrators, staff, and former patients so they can talk as a group.
Purposive sampling would seek out people that have each of those attributes. Quota sampling takes purposive sampling one step further by identifying categories that are important to the study and for which there is likely to be some variation.
In this nonprobability sampling method, subgroups are created based on each category, the researcher decides how many people to include from each subgroup, and then collects data from that number for each subgroup. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves and instead eat in a dorm cafeteria. As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements.
Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. In addition, it is possible that your review of literature on the topic suggests that campus housing experiences vary by gender. Your quota sample would include five people from each of the four subgroups. In , up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods.
For that, you need probability sampling, which we will discuss in the next section. Qualitative researchers can also use snowball sampling techniques to identify study participants.
In snowball sampling , a researcher identifies one or two people they would like to include in their study but then relies on those initial participants to help identify additional study participants. In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included. This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias.
That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.
Non-probability sampling techniques are often used in exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population. A convenience sample simply includes the individuals who happen to be most accessible to the researcher. You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic.
This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.
Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves e. Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others. You send out the survey to all students at your university and a lot of students decide to complete it. This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.
It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific.
An effective purposive sample must have clear criteria and rationale for inclusion. You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.
If the population is hard to access, snowball sampling can be used to recruit participants via other participants. You are researching experiences of homelessness in your city. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people that she knows in the area.
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population. Samples are used to make inferences about populations.
Samples are easier to collect data from because they are practical, cost-effective, convenient and manageable. Probability sampling means that every member of the target population has a known chance of being included in the sample. Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling. In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.
Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling. In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.
This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings e. Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes. Do the check. Generate your APA citations for free! APA Citation Generator. Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans.
Journal of Adolescent Research, 26 , 30— Initial participants were given coupons to pass on to others they knew who qualified for the study. Using this strategy, Kogan and colleagues succeeded in recruiting study participants. Quota sampling A nonprobability sample type for which a researcher identifies subgroups within a population of interest and then selects some predetermined number of elements from within each subgroup. When conducting quota sampling, a researcher identifies categories that are important to the study and for which there is likely to be some variation.
Subgroups are created based on each category and the researcher decides how many people or documents or whatever element happens to be the focus of the research to include from each subgroup and collects data from that number for each subgroup. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves but eat in a dorm cafeteria.
As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements. Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. It is possible as well that your review of literature on the topic suggests that campus housing experiences vary by gender.
Your quota sample would include five people from each subgroup. In , up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods.
The leading polling entity at the time, The Literary Digest , predicted that Alfred Landon would beat Franklin Roosevelt in the presidential election by a landslide. Van Allen, S. Gallup corporate history. Basics of social research: Qualitative and quantitative approaches 2nd ed. Boston, MA: Pearson. If you are interested in the history of polling, I recommend a recent book: Fried, A. Pathways to polling: Crisis, cooperation, and the making of public opinion professions. New York, NY: Routledge.
While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, it would be a mistake to think of this strategy as yielding statistically representative findings. Finally, convenience sampling A nonprobability sample type for which a researcher gathers data from the elements that happen to be convenient; also referred to as haphazard sampling.
0コメント