Which of the following sampling techniques is most likely to result in a biased sample?
Published on May 20, 2020 by Pritha Bhandari. Revised on October 17, 2022. Sampling bias occurs when some members of a population are systematically more likely to
be selected in a sample than others. It is also called ascertainment bias in medical fields. Sampling bias limits the generalizability of findings because it is a threat to
external validity, specifically population validity. In other words, findings from biased samples can only be generalized to populations that share characteristics with the sample. Your choice of research design
or data collection method can lead to sampling bias. This type of research bias can occur in both probability and non-probability sampling. In probability sampling, every member of the population has a known chance of being selected. For instance, you can use a random number generator to select a simple random sample from your population. Although this procedure reduces the
risk of sampling bias, it may not eliminate it. If your sampling frame – the actual list of individuals that the sample is drawn from – does not match the population, this can result in a biased sample. Although you used a random sample, not every member of your target population –undergraduate students at your university – had a chance of being selected. Your sample misses anyone who did not sign up to be contacted about participating in research. This may bias your sample towards people who have less social anxiety and are more willing to participate in research. Sampling bias in non-probability samplesA non-probability sample is selected based on non-random criteria. For instance, in a convenience sample, participants are selected based on accessibility and availability. Non-probability sampling often results in biased samples because some members of the population are more likely to be included than others. Example of sampling bias in a convenience sampleYou want to study the popularity of plant-based foods amongst undergraduate students at your university. For convenience, you send out a survey to everyone enrolled in Introduction to Psychology courses at your university. They all complete it in exchange for course credits.Because this is a convenience sample, it is not representative of your target population. People who take this course may be more liberal and drawn towards plant-based foods than others at your university. Types of sampling bias
How to avoid or correct sampling biasUsing careful research design and sampling procedures can help you avoid sampling bias.
Oversampling to avoid biasOversampling can be used to avoid sampling bias in situations where members of defined groups are underrepresented (undercoverage). This is a method of selecting respondents from some groups so that they make up a larger share of a sample than they actually do the population. After all data is collected, responses from oversampled groups are weighted to their actual share of the population to remove any sampling bias. Example of oversampling to avoid sampling biasA researcher wants to study the political opinions of different ethnic groups in the US and focus in depth on Asian Americans, who make up only 5.6% of the US population. The researcher wants to study each ethnic group separately, but also gather enough data about Asian Americans for precise conclusions.They gather a nationally representative sample, with 1500 respondents, that oversamples Asian Americans. Random digit dialling is used to contact American households, and disproportionately larger samples are taken from regions with more Asian Americans. Of the 1500 respondents, 336 are Asian American. Based on this sample size, the researcher can be confident in their findings about Asian Americans. Weighting is applied to ensure that the responses of Asian Americans account for 5.6% of the total. This allows for accurate estimates of the sample as a whole. Frequently asked questions about sampling biasWhat is sampling? 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 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population. Why are samples used in research? Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable. Cite this Scribbr articleIf you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Is this article helpful?You have already voted. Thanks :-) Your vote is saved :-) Processing your vote... Which sampling technique is most likely to result in biased sample?Non-probability sampling often results in biased samples because some members of the population are more likely to be included than others.
Which of the following sampling methods are biased?Two examples of sampling methods that produce biased samples are voluntary response sampling and convenience sampling.
What are the 3 types of sampling bias?Types of Sampling Bias. Observer Bias. Observer bias occurs when researchers subconsciously project their expectations on the research. ... . Self-Selection/Voluntary Response Bias. ... . Survivorship Bias. ... . Recall Bias.. What makes a sample biased?Sampling bias or a biased sample in research occurs when members of the intended population are selected incorrectly – either because they have a lower or a higher chance of being selected. The most popular and easily understandable example of sampling bias is Presidential election voters.
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