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Lecture 8

KINE 2049 Lecture 8: Lecture 8, Getting a sample, October 16th
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Department
Kinesiology & Health Science
Course Code
KINE 2049
Professor
Merv Mosher

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Description
KINE 2049 F October 16th Lecture 8, Getting a sample Having a bigger sample is usually better for getting more accurate data, but what really matters is that it is representative of your population Samples should be more than 30 people, less than 50% of the population, and be representative. If you can do 50% of the population, you might as well do all the population Random selection is the best way to select for a representative population, and it has two factors that must be followed every person or subject that youre dealing with has an equal chance of being selected the selection of one does not bias the chance of the others in being selected Selection types simple random selection: putting everyones name in a hat. The problem with this is that you need to be careful of the size of the paper you use. It has to be the same size for everyone, regardless of the length of their name With replacement: every time you select a name, you put it back in the hat Without replacement: every time you select a name, you put it aside, not in the hat, which makes the odds not the same for each person Stratied random selection: You divide the population into dierent subgroups based on characteristics or traits, like gender or religion. Say you have a group of 70% women and 30% men. To sample this, you can take 7 women and 3 men, which is still the same ratio of the population Systematic sampling: you use a system, like taking every 10th person from a group Cluster sampling: Assume you want to sample teachers in the Toronto area, and theres a 1000 schools. You do cluster sampling, which is taking the names of all the schools in a hat, and randomly selecting 10 of them. You then survey every teacher in the 10 schools that you sampled out of the 1000 Multistage sampling: assume each school has 100 teachers, so thats a 1000 interviews. You can break it down again by taking 10 teachers in each school, and sampling them. So you have to do 100 interviews in total Non probability sampling examples: deliberate sampling: like the Nurses health study where they only sampled nurses. You select subjects with specic characteristics that you are studying Convenience sampling: You pick subjects that are easy for you to get into contact with
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