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Lecture 12

# SOC-1101 Lecture Notes - Lecture 12: Stratified Sampling, Systematic Sampling, Cluster Sampling

School

University of WinnipegDepartment

SOCIOLOGYCourse Code

SOC-1101Professor

Anna BorisenkovaLecture

12This

**preview**shows half of the first page. to view the full**2 pages of the document.**Research Method - INTERVIEWS

SAMPLING

Sample is a small proportion of the overall group. One can usually be confident

that results from a population sample, as long as it was properly chosen, can

be generalized to the total population.

REASONS FOR SAMPLING:

• All members of a population may not be available

• Cheaper

• Less time consuming

PROBABILITY SAMPLING

Random sampling - an example of random sampling would be picking a name

out of a hat. In random sampling everyone in the population has the same

chance of getting picked. This is easy because it is quick and can even be

performed by a computer. However, because it is down to chance you could

end up with a unrepresentative sample, perhaps with one demographic being

missed out

Systematic sampling - an example of systematic sample would be picking every

10th person on a list or register. This carries the same risk of being

unrepresentative as random sampling, for example, every 10th person could

be a girl.

Stratified sampling - this method attempts to make the sample as

representative as possible, avoiding the problems that could be caused by

using a completely random sample. To do this the sample frame will be divided

into a number of smaller groups, such as social class, age, gender, ethnicity,

etc. individuals are then drawn at random from these groups.

Cluster sampling - this is taking a random sample at various stages of the

sampling process.

NONPROBABLITY SAMPLES

Haphazard - get any cases in any matter that is convenient.

Quota - get a present number of cases in each of several predetermined

categories that will reflect the diversity of the population, using haphazard

methods.

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