This assumption is the proportion of the total population that is in a category. There is a fundamental assumption made for quota sampling. Once the quotas are full, no more items of data can be added to the quota, and so they are ignored. To do this, participants are assessed and then allocated into the appropriate quota. In quota sampling, the person collecting the data selects a sample that reflects the characteristics of the whole population. There are two types of non-random sampling: quota sampling and opportunity sampling. Non-random sampling is a non-probability sample method. We need to be able to justify why a particular size of sample is good or bad.Īs a rule of thumb, the bigger the sample, the more reliable it will be. There is no specific value that gives you a representative sample size compared to the population. For example it is more likely to have a good wi-fi signal in an urban area because there is higher demand for the services, but it is more likely to find taller trees and a wider range of wildlife in rural areas. On the other hand, if we gathered data from everyone who lived in the “urban dwelling” subset, we would be asking 82.37\% of the population of England.Īsking a greater number of people a question will obtain a much more accurate representation of the population, however, it will take a very long time, can be very expensive and depending on what you are researching, can hold a significant bias. If we were to gather data from every resident in the “ rural village” subgroup, we would only be asking only 0.29\% of the total population of England. 157,743 of these people lived in a “ rural village in a sparse setting” and 43,668,600 people lived in an “urban dwelling”. Individuals have to remain local to the area of research with a definite boundary (no radical changes in the population due to births / deaths / migration).Ī sample should be a representation of a population and so the more individuals that are in the sample, the more accurately it will represent the population.Īccording to the 2011 UK Census, there were 53,012,456 people living in England. Subject to bias (skewed results) leading to an unfair representation of the population. Volunteers are accessible and quick to return data. The first member of the population must be chosen at random to avoid bias. The sample can be selected proportional to the total population (stratified).Įvery member of the population must be listed. Requires another sampling method to select individual items of data from a list (random / systematic etc.) Proportional representation means the sample is representative of the population so the results can be generalised. Not always possible if there is no sampling frame or list to sample from. It is more time efficient than asking the entire population. Random selection means the results can be generalised for a population. Another sample of woodlice was captured 5 days later and the number of marked woodlice was counted.įollowing any particular sampling methodology has a variety of advantages and disadvantages: Volunteers usually collect data.Īsking people at a given location about how long their commute to work is.Ĭollecting a sample of data from one location at different points in time, marking the individuals to estimate a population size.Ī sample of woodlice were captured, marked and released. The 5th person is chosen randomly, followed by every subsequent 8th person.Ĭonvenience sampling is used for ease of data collection. After the first member is chosen at random, the remaining members are chosen from a given interval.Ī list of people with their first names in alphabetical order are numbered. Smaller groups or strata within the sample are represented proportionally to the population.įinding out a favourite soap opera from different age categories of people in a year group.Įvery member in the population is given a number. Using a random number generator to select students in a class to complete a task. Gathering a representative sample from a population where each member in the population has an equal chance of being selected. Random sampling (aka simple random sampling) In order to collect data there are several types of probability sampling methods and non-probability sampling methods we can use:īelow is a brief summary of each sampling method.
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