The resulting sample may not vary greatly in terms of workload. In a study on stress and workload, employees with high workloads are less likely to participate. People who refuse to participate or drop out from a study systematically differ from those who take part. People who are more thrill-seeking are likely to take part in pain research studies.
People with specific characteristics are more likely to agree to take part in a study than others. People who take this course may be more liberal and drawn towards plant-based foods than others at your university. They all complete it in exchange for course credits.īecause this is a convenience sample, it is not representative of your target population. For convenience, you send out a survey to everyone enrolled in Introduction to Psychology courses at your university. Example of sampling bias in a convenience sampleYou want to study the popularity of plant-based foods amongst undergraduate students at your university. Non-probability sampling often results in biased samples because some members of the population are more likely to be included than others. For instance, in a convenience sample, participants are selected based on accessibility and availability. Sampling bias in non-probability samplesĪ non-probability sample is selected based on non-random criteria. This may bias your sample towards people who have less social anxiety and are more willing to participate in research. Your sample misses anyone who did not sign up to be contacted about participating in research.
#SIMPLE RANDOM TECHNIQUE GENERATOR#
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.Īlthough you used a random sample, not every member of your target population –undergraduate students at your university – had a chance of being selected. Example of sampling bias in a simple random sampleYou want to study procrastination and social anxiety levels in undergraduate students at your university using a simple random sample. 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. For instance, you can use a random number generator to select a simple random sample from your population.Īlthough this procedure reduces the risk of sampling bias, it may not eliminate it. In probability sampling, every member of the population has a known chance of being selected. This type of research bias can occur in both probability and non-probability sampling. Your choice of research design or data collection method can lead to sampling bias.
An example of such a sample is interviewing people in the street when only those whom the researcher finds easily accessible are selected.Īnother probable plan, quota sampling, involves identifying relevant groups among the whole population to “capture diversity among units” (Neuman 249). However, this method is reported to produce “very nonrepresentative samples” quite frequently (Neuman 248). In this approach, cases are readily available and easy to reach (Neuman 248). Convenience sampling, which is also called accidental or haphazard, is less suitable for the present study. Other potential sampling plans that might be used are convenience and quota sampling. Opting for an SR sample is appropriate in the given case since it will enable the researcher to mimic the US population in terms of the proportion of different ethnicities. Thus, in the present scenario, it may be necessary to contact the specific household several times to engage it in research.
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