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University of Guelph
Sociology and Anthropology
SOAN 2120
David Walters

November 27, 2012 Handing in the Final Assignment - due outside of class Thursday (1-4pm) - the late penalty is 5% per day - after 5 pm on Thursday, it is LATE - after the deadline, submit the assignment under the door (614 MACK) - o ID: 5632747 o Password: soan2120 - TOMORROW AFTERNOON (LAB): 20 MACK – 1 PM – 4PM - Chapter 5 [ THIS SHIT IS IMPORTANT] o Nonprobability sampling  Sample size in advance –they do not know the sample size in advance  Random – they do not know if it is random  Knowledge of population – they know very little  Mathematical theory  Types of nonprobability samples (types, page 109) o Probability sampling [EXACT OPPOSITE OF NONPROBABILITY]  Sample size in advance – must have this in advance  Random – must have  Knowledge of population  Mathematical theory – sampling methods are based heavily on this o Goal of Probability Sample?  To make  Populations, elements, and sampling frames  Population: boundaries  Geographical location  Businesses in Ontario  Employers at an organization  Students in the school  People who became parents I 2012 o Populations are abstract  Variable  Nevertheless, we need to estimate it  Generate list of what the population looks like  The list is the sampling frame (telephone directories, tax records, driver’s license records, etc.)  Sample element: unit of analysis within the population  Sample frame should be a good representation of the population  What if it’s not? o Eg. telephone directories? Not everyone has a telephone.  Sampling technique in order to collect sample  We want it to estimate the probability that everyone in the population has a chance to be in our sample  SRS (simple random sampling)  Assign a number to each element in the sampling frame  Use a random number generator  Equal probability  Benefit of Random Sampling – sampling distribution (clt) – inferences  Only with random sampling are the results we get from our statistical test valid. The central limit theorem applies only in the context that our sample was taken randomly or through the weights….  Systematic sampling  First decide on your sample size  Population 1000  Sample 100  Sampling interval: 10  Start with your sampling frame (number each element): pick one random number and then every 10 thereafter  Stratified Sampling  Divide population into sub-populations and then sample within stratum  Stratum are picked by the researcher o Eg. provinces, educational groups, religious organizations, etc. o So you stratify your population in these groupings – this guarantees you get one of each  Guarantees stratums are represented in the survey (a certain number of people are chosen from each stratum… like P.E.I., Inuit, Ph.D)  Weight variables allow us to adjust probability…  Cluster Sampling  Identify clusters (i.e. cities, blocks, households) o Simple random sample clusters o Simple random sample units within clusters  Population Guelph o 1: City blocks o 2: households (within the blocks) o 3: individuals (within the households)  implications: we get three levels of sources of random error Sampling Designs (City of Guelph)   stratify(left)  sample within stratum  more diverse selection of the individual within the design  cluster (right) Comparing the methods •!Cost effectiveusters, then within clusters  get tightly packed individuals within a cluster o Comparing the methodsive  Cost effective  Representativeions are usually a  Sampling decisions are usually a tradeoff between accuracy (how accurate does it represent the entire population)and efficiency (of collecting the data) PRACTICE MULTIPLE CHOICE TEST ONLINE TOMORROW!**************** Components of this Class 1) Research pro
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