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An insight into the polling results conducted by ses research during the 2007 canadian provincial and federal elections. It discusses the importance of sampling techniques, the history of sampling, and various types of sampling designs such as simple random sampling, systematic sampling, and stratified sampling. The document also touches upon non-probability sample designs and measurement and questionnaire design.
Typology: Lecture notes
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On January 22, 2006 the research firm SES sampled 1,200 Canadians on their voting intentions in the upcoming Federal election. These were the results of the poll:
The company claimed these estimates accurate to within +/- 3 percentage points 19 times out of 20.
Q. How could SES Research, with a tiny sample of 1200 predict with amazing accuracy the voting behavior of several million Canadians…and ruin much of the suspense on election night?
A. With careful sampling techniques!!!
Sampling has developed in step with political polling… elections allow researchers to test sampling designs.
The infamous Literary Digest Presidential poll (a huge sample that was embarrassingly wrong in its Presidential prediction).
Gallup: from quota to probability sampling.
Probability Sampling: A sample will be representative of the population from which it is drawn if all members have a known (to the researcher), non-zero chance of selection.
In addition to problems with sampling people who are “convenient” to the researcher, a researcher’s personal biases (conscious & unconscious) may affect selection in ways that make the sample unrepresentative of the population.
Sampling bias means those selected into the sample are NOT typical or representative of the larger population from which they have been chosen…and usually the population that the researcher wants to generalize results to!
A sample is representative if sample characteristics closely resemble population characteristics.
This can only happen if ALL members of the population have a known, non-zero chance of being selected into the sample (probability samples).
EPSEM samples ensure that all members of the population have an equal-chance of being selected.
Probability samples are always more representative than non-probability samples and allow researchers to estimate the margin of error (e.g. + / - 3 percentage points 19 times out of 20).
Simple Random Sampling (SRS)
Simple random sampling ( SRS ) is the basic probability design and is incorporated at some stage in ALL probability sampling designs.
Each unit has an: n / N chance or probability of being selected into the sample….where n = the size of the sample; and N = the size of the population. (e.g., n = 500; N = 300,000; chance of being selected is 500 / 300,000 = .0016)
With SRS , you need an accurate and complete sampling frame ….each element in the population of interest is listed once and only once!
Simple Random Sampling: Assign a unique number to each sampling unit & select sampling unit numbers using a random number table or generator.
Systematic Random Sampling: Determine the sampling interval; select the first unit randomly, select remaining units using interval increments.
Stratified Random Sampling: Determine strata; select from each stratum a random sample proportionate (or disproportionate) to the size of the stratum in the population of interest.
Multi-stage Area Sampling: Determine the number of levels or areas and from each level or area select randomly.
sampling frame after a random start. (e.g., you want to select a sample of 100 persons from a population of 10,000. After a random start between 1 and 100, you will select every one hundredth individual..( k = N / n = 10,000 / 100 = 100)….where: k is the sampling interval; N is the size of the population; and n is the size of the sample.
If your random starting number was 14, you will pick the 14th^ person on your list, followed by the 114 th^ person, followed by the 214th^ person, and so on until you have drawn your sample of 100 people.
With stratified sampling we could ensure that in a
sample of 100 students, we obtain 70 (70%) from Ontario, 20 (20%) from other provinces, and 10 (10%) from outside Canada by randomly selecting within these three strata.
This is known as proportionate stratified
sampling.
regarded as comprising a hierarchy of sampling units (e.g., a university can be broken down into faculties, departments, sections and classes; Canada can be broken down into provinces, counties or regions, cities, city blocks, and households).
With cluster sampling we randomly sample down
the hierarchy of sampling units in a population of interest.
select cities from this list.
Within each of the selected cities, compile a list of
all residential city blocks and randomly select a number of residential city blocks.
Within each of the selected residential city blocks,
compile a list of all households and randomly select our sample of households.
