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Chapter 12

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University of Toronto St. George
Deborah Leslie

Week 4 Reading Notes Saturday, February 23, 2013 12:39 PM Chapter 12 - Sampling Sample: The segment or subset of the population selected for research. The selection may be based on either probability or non-probability sampling. Sampling error: difference between sample and population even though a random process was used Sampling frame: the listing of every unit from which the sample is being drawn Non-Sampling Error: difference between population and sample caused by either deficiencies in sampling approach, non-response, or poorly worded questions Representative sample: a sample that represents the larger population Probability sample: a sample selected through a random process so that each unit has an equal chance of being included Purposive sample/non-probability sample: A sample not selected using random sampling method. This implies that some units in the population are more likely to be selected than others Problems of sampling in studies involving structured interviews or questionnaires:  Not using random method to pick sample - there is a risk that the selection process will be affected by human judgment, so that some members of the population are more likely to be selected than others.  The sampling frame or list of potential subjects is inadequate  Some people in the sample refuse to participate or cannot be contacted Types of probability sample: Simple Random Sample  Most basic form of sampling  Each unit of the population has an equal probability of inclusion in the sample  A sample in which each unit selected from the population and each combination of units has an equal probability of being included.  There is almost no opportunity for bias since the selection process is entirely random. Systematic Sample  sample is selected directly from the sampling frame  It is important to ensure that there is no inherent ordering or pattern in the sampling frame, a feature called periodicity.  With systematic sampling, not every possible combination of cases has an equal chance of being selected. Stratified Random Sampling  A sample in which units are randomly sampled from a population that has been previously divided into sub-groups (strata)  It is feasible only when it is relatively easy to identify and allocate units to sub- groups  It ensures that the sample is distributed in the same way as the population in terms
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