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.

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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.

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Causes of sampling bias

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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.

Sampling bias in probability samples

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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.

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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.

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Example of sampling bias in a simple random sample

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You want to study procrastination and social anxiety levels in undergraduate students at your university using a simple random sample. You assign a number to every student in the research participant database from 1 to 1500 and use a random number generator to select 120 numbers.

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.

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Sampling bias in non-probability samples

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A non-prbability sample is selected based on non-random criteria. For instance, in a convenience sample, participants are selected based on accessibility and availability.

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Non-probabilty 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 sample

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You 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.

How to avoid or correct sampling bias

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Using careful research design and sampling procedures can help you avoid sampling bias.

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O Define a target population and a sampling frame (the list of individuals that the sample will be drawn from). Match the sampling frame to the target population as much as possible to reduce the risk of sampling bias.

O Make online surveys as short and accessible as possible.

O Follow up on non-responders.

X Avoid convenience sampling.

Oversampling to avoid bias

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Oversampling 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.

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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 bias

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A 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.

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CR: Bhandari, P. (2023, March 17). Sampling Bias and How to Avoid It | Types & Examples. Scribbr. Retrieved August 21, 2023, from https://www.scribbr.com/research-bias/sampling-bias/