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October 10,2013 - 3N06 Lecture Notes.doc

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Department
Political Science
Course
POLSCI 3N06
Professor
Todd Alway
Semester
Fall

Description
October 10, 2013 Political Science 3N06 O CTOBER 10, 2013 Political Science 3N06 2013 Lecture 6a A Sample of Sampling What do you do in a circumstance where there are too many elements to observe? Why Sample? - Social Scientists are frequently interested in understanding things that are too large to study directly - Like, for example, Canadians - If you want describe the attitude of all Canadians, how do you do so? - You could ask each and every Canadian, but this would prove to be a very cumbersome, expensive task - However, what if it were possible to understand the entire population by studying only a small representative portion of the larger group? o Whether the population you are interested in is all Canadians, or all American Foreign Policy Documents, or all news broadcasts on the CBC? S AMPLING - S AMPLING is the technique for consciously selecting who or what, amongst all the elements contained in the population of interest, that you will actually study - Who to survey, who to interview, who/what to observe Two types of samples: Probabilistic and Non-probabilistic P ROBABILISTIC o QUANTITATIVE RESEARCH o Getting a representative sample; the small sample has to resemble the larger; need to be able to conclude from the large to the small. o Random Sampling – it does not mean chaotic. There has to be a procedure to be followed. - This is particularly common in quantitative research - However, even in qualitative research, if the goal is to generalize your findings to a larger group 1 October 10, 2013 Political Science 3N06 o Probabilistic sampling is the best way to do this - The technique is designed to produce a sample that is representative of a larger population - If successful, conclusions drawn from the sample can be generalized to the larger population (within the limits of sampling error) N ON -PROBABILISTIC Qualitative - Some (but not all) qualitative studies rely on non-probabilistic samples - A common critique is that such samples might be unrepresentative and therefore be non-generalizable o Although representativeness and generalizability are not always the goals of qualitative research 1) P ROBABILISTIC S AMPLES - Requires an appropriate sampling frame o One in which every element in the population has a known probability of being selected A) Simple Random Sampling - All elements in the sampling frame (i.e. the population of interest) have an equal probability of being selected for inclusion in the sample - Inclusion is based upon some random selection mechanism, like a table of random numbers - It cannot be chaotic; o You need to have accurate information about the population that will be generalized about o A frame; every element of the population has to have the possibility of being selected. o The process of determining who is on the list; for example; those who are not on the voting poll; who is not on the sampling frame and cannot make it inside  CANADA VOTERS; etc. poor, uneducated, homeless, etc. 2 October 10, 2013 Political Science 3N06  Who is included and who is excluded? If there is a coverage problem cannot generalize from the smaller population to the larger. o Chance and chance alone should determine who is going to represent it. o Depending upon the population you want to generalize, need to have a proportion that will be representative of the population. Need to have a sample of a certain size. The larger the proportion o Random does not mean haphazard; chance and chance alone needs to decide;  Randomness is difficult to generate; B) Systematic Sampling - Like simple random s
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