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Lecture 5

Lecture 5 Notes

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SOCI 313
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Lecture 05 SOCI 313 July 17 , 2013 Population and Sample -whom and what will we collect data from -describe in terms of variation -knowing about a population, takes a lot of effort to capture variation, really dependson how much variation is in it -if the population is homogneous, then the process can be incredibly simple -develop a sample where we will capture variation -than generalized back to general population, estimate the error in doing that -fundamental problem behind sampling is to find the same variations that occur in the population -no way we generalize without it =in orer for us to properly respresent what is going in the population, have to make sure there is no error -biggest source of error is some form of human bias, randomize your sampling, leave the decisions to chance -preferneces bias your ability to go out and collect data -understnading of population there fore differe, mustdo this by removing human decision making by using randomization to rule it out -bias not only affects our ability go generalize to the larger population also effects our ability to make the relationship between x and y Sampling frames and random sampling -bias of human erroridn’t have a large enough sample, general rule is at minum 30 people in a sample because you can be assured that random selection has actually occurred -assumption in smaller populaitons, refence to large population -need to have certain number of elemnts drawn into your smaple in order to make it accurately represent the population Sampling Error -simple random sampling is theoretically the best way to create a represetntative sample of a population, because it minimizes sampling error -random sampling minimizes theoretical sampling error -systematic error is a human bias -is a bias in your sample, obviously unrepresentative of the variation in the population attribute that has occurred because of other reasons -more likely that some elements will be systematically drawn than others -best way to remove random error is to increase sample size, really comes down to in absolute terms an continue to perfect terms by increasing sample size, but there is a limit determined by a payout -diminishing returns in terms of human labout, very minimal improvements when looking at extremely large samples -national polls only use about 10,000 people, only want to know that estimate for the population are precise -but for sociology for generating theory, a 1-2G is just as good as 10G -for theory generation -do not need accuracy, and do not have means and money to do so either -random pull from pull into sample population is randomly designed, by every other derivation si designed -systematic sample with a random start periodicity human error -oeridoicitiy occurs because elements are lined up due to eg gender and racialized status -sampling frame sorting also impacts bias, can also create a bias -look at males vs females eg for stratified sample -the researcher first divides the population into grouping or strata of interest -then samples from a sampling frame How to overcome random stratification: using a census instead of a sample -one to one ratio, for a smaller population -disproporational sample, can properly represent a smaller group in comparison to larger ones -although lose the abi
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