���z���ʼn�, � �/�|f\Z���?6�!Y�_�o�]A� �� PK ! Even the professor used probability sampling methods, your school may differ from other schools in important ways. But stratified and simple random sampling, in general, produce a more precise estimate than cluster sampling for the same sample size. Example: If you get a piece of apple that is green, you’d assume that the whole apple is green. Using stratified sampling allows us to make sure our sample has the same proportion of people from each class year as the overall population of the school. Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans. A number calculated with complete population data and quantifies a characteristic of the population is called which of the following? When we use stratified sampling, we are concerned with how well our sample reflects the population. Stratified samples can better account for periodicity by creating strata that reduce or eliminate the effects of periodicity. In fact, generalizability is perhaps the key feature that distinguishes probability samples from nonprobability samples. Your school likely has a list of all of the fraternity members on campus, as Greek life is subject to university oversight. a. This formula leads us to a selection interval of 7. To draw a systematic sample, you’d simply select every kth element on sampling frame list. From these examples, we can see that nonprobability samples are useful for setting up, framing, or beginning any type of research, but it isn’t just early stage research that relies on and benefits from nonprobability sampling techniques. That would be a case of sampling error—a mismatch between the results of the sample and the true feelings of the overall class. A simple random sample requires a real sampling frame—an actual list of each person in the sampling frame. 2LNj}Ԋ.�70�8�8$V���$|N!�t4hqi�}�>�����M޸E�!��7�����W���4�da���"ڳ�����ɋ�o�fkG�=/+b�=�#Kx��(�Uf���Xfә If the cluster sample is stratified, then the numbers in this probability formula reflect those in a particular stratum. In our binge drinking example, we would need to know how many students are in each class year to make sure our sample contained the same proportions. Scientific Inquiry in Social Work by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. Which of the following sampling methods is the best way to select a group of people for a study if you are interested in making statements about the larger population? They both are good, but for different purposes. When you take a look, you know immediately that they were distributed by chance. This strategy is also the most suitable when the members of the population are similar to the important variable or variables. It is worthwhile to note that each stage is subject to its own sample error, so choosing to sampling in multiple stages could yield greater error. Generalizability refers to the idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. ]��N�[�|u��B��� As a result, in cluster sampling, the selection process is subject to two levels of chance variation, one for the selection of clusters and one for the selection of elements within a cluster. The larger the sample size, the greater the sampling error b. You probably know that college classes are different sizes. [1] Among other problems, Gallup’s quota categories did not represent those who actually voted (Neuman, 2007). True randomness is difficult to achieve, and it takes complex computational calculations to do so. 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. There is one clear instance in which systematic sampling should not be employed. Which of the following formulae is used to determine how many people to include in the original sampling? The researcher can only yield perfect results if they include everyone from the target population into the sample, which defeats the purpose of sampling. a. How often does the Census Bureau take a complete population count? 5.3 Ethics at micro, meso, and macro levels, 5.4 The practice of science versus the uses of science, 6.2 Paradigms, theories, and how they shape a researcher’s approach, 7.3 Unit of analysis and unit of observation, 8. How might you go about getting a probability sample of fraternity members that is representative of the overall population? For example, a researcher interested in studying how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting an ad in the newspaper or by announcing the study at a social gathering. Name lab. Let’s assume that in the population, there are 45% Republicans and 55% Democrats. While convenience samples offer one major benefit—convenience—they do not offer the rigor needed to make conclusions about larger populations. Would that be a representative sample of all students in the class? See Family Education. The variance of an estimator of the population mean is a function of the number of clusters selected, the sample size within each cluster, and the ICC. In which of the following nonrandom sampling techniques does the researcher ask the research participants to identify other potential research participants? Researcher selects cases from within several different subgroups. For instance, if you’re researching how members of a local book club compare ebooks and traditional ink-on-paper books, you could contact one of the members and ask them to introduce you to other members. I know precisely how such tasks can make you feel totally lost, so in this article, I did my best to provide you a comprehensive guideline to sampling strategies, including: Let’s get to know to sample a bit closer. Cluster sampling can be applied in one or more stages but, regardless of the number of stages, the first step is to select the clusters (primary sampling units) from which sample elements (secondary sampling units) will be drawn. The downside of this simple approach is that it results in differing sample sizes per cluster, making it less attractive than other designs. Proportional stratified sampling c. Cluster random sampling d. Systematic sampling 18. http://www.pbs.org/fmc/timeline/e1948election.htm. 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. Which of the following is not a type of nonrandom sampling? ), Non-numerical data typically answers the questions like, “What? 178), how many participants will you need for a research study with a population of 120,000? A representative sample resembles important characteristics of the population from which it was drawn, in ways that are important for the research being conducted. (Bias will be discussed in more depth in the next section.) While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory research), quantitative researchers tend to rely on probability sampling techniques. 2. In 1936, up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods. A statistic c. A parameter d. A population 20. It’s often compelling to even find relevant data, let alone collect it in a specific way! Interval is defined by dividing the population size on the desired sample size. Dependent variable c. Stratification variable d. Sampling variable 19. �{M�5�b.�H��4��R����bR ���|qobr� d@^�vBхv↰�:���sîR�|6ؕ py�����`ė�LF�l0�\�� ��8�X�T�Y�I� ]$��:�h]���2Yf(����O���X�a��� g����R@� �7����+8#w)U~ Even if you could find a way to generate such a list, attempting to do so might not be the most practical use of your time or resources.

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