![]() ![]() However, non-probability sampling methods tend to be cheaper and more convenient, and they are useful for exploratory research and hypothesis generation. Consequently, you cannot estimate the effect of sampling error and there is a significant risk of ending up with a non-representative sample which produces non-generalisable results. In non-probability (non-random) sampling, you do not start with a complete sampling frame, so some individuals have no chance of being selected. Probability sampling methods tend to be more time-consuming and expensive than non-probability sampling. In this way, all eligible individuals have a chance of being chosen for the sample, and you will be more able to generalise the results from your study. In probability (random) sampling, you start with a complete sampling frame of all eligible individuals from which you select your sample. There are several different sampling techniques available, and they can be subdivided into two groups: probability sampling and non-probability sampling. For example, if the electoral roll for a town was used to identify participants, some people, such as the homeless, would not be registered and therefore excluded from the study by default. This may involve specifically targeting hard to reach groups. If a sample is to be used, by whatever method it is chosen, it is important that the individuals selected are representative of the whole population. (Calculation of sample size is addressed in section 1B (statistics) of the DFPH syllabus.) Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. It would normally be impractical to study a whole population, for example when doing a questionnaire survey. The numbers you selected then correspond to the numbers assigned to the members of your population, and those selected become your sample.We are currently in the process of updating this chapter and we appreciate your patience whilst this is being completed. Continue this way through the table until you have selected your entire sample, whatever your n is.If the number is 84301, you would use it and you would select the person in the population who is assigned the number 301. ![]() ![]() You would skip this number and move to the next one. If the number on the table was 23957, you would not use it because the last 3 digits (957) is greater than 350. Put another way, if your population contained 350 people, you would use numbers from the table whose last 3 digits were between 0 and 350. For instance, if N is a 3 digit number, then X would be 3. Select the first n numbers (however many numbers are in your sample) whose last X digits are between 0 and N.Choose a direction in which to read (up to down, left to right, or right to left).Whichever number your finger is touching is the number you start with.) (The best way to do this is to close your eyes and point randomly onto the page. Select a starting point on the random number table.Determine the population size and sample size.Number each member of the population 1 to N. ![]()
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