In this case, since children from urban areas form 80% of the population, you will have to weigh their results four times more than those of the children from rural areas. If you choose an equal number of units, keep in mind that you need to weigh the results in order to draw conclusions for the population as a whole. Then, you can continue with your data collection (e.g., ask them to fill in a questionnaire). Select 80 urban and 20 rural, which gives you a representative sample of 100 people.Select 100 urban and 100 rural, i.e., an equal number of units.Then, you take a simple random sample from each subgroup. If you take a simple random sample, children from urban areas will have a far greater chance of being selected, so the best way of getting a representative sample is to take a stratified sample.įirst, you divide the population into your strata: one for children from urban areas and one for children from rural areas. As you look at a list of all the youth players in your state, you notice that there are 32,000 children from urban areas and 8,000 children from rural areas. You want to know if children from urban areas are more likely to play than children from rural areas. By selecting units from each subgroup equal to their proportion in the total populationĮxample: Stratified samplingYou are investigating why young people choose to play basketball.By selecting an equal number of units from each subgroup.Then you can select your sample from each subgroup. To split your population into different subgroups, first choose which characteristic you would like to divide them by. This way, you can ensure that all subgroups of a given population are adequately represented within your sample population.įor example, if you are dividing a student population by college majors, Engineering, Linguistics, and Physical Education students are three different strata within that population. Each subgroup is separated from the others on the basis of a common characteristic, such as gender, race, or religion. Stratified sampling collects a random selection of a sample from within certain strata, or subgroups within the population. You continue by matching each number with the respective resident on the list. If the first number generated by the program is 1735, this means that resident #1735 on your list should be selected to be part of the sample. Instead, you decide to use a random number generator to draw a simple random sample. Writing down the names of all 4,000 inhabitants by hand to randomly draw 100 of them would be impractical and time-consuming, as well as questionable for ethical reasons. You have established that you need a sample of 100 people for your research. You have access to a list with all 4,000 people, anonymized for privacy reasons. Example: Simple random samplingYou are researching the political views of a municipality of 4,000 inhabitants. There are several free ones available online, such as, , and. To compile a list of the units in your research population, consider using a random number generator. This is the most common way to select a random sample. Simple random sampling gathers a random selection from the entire population, where each unit has an equal chance of selection. There are four commonly used types of probability sampling designs:
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