# SOC 232 Lecture Notes - Census Geographic Units Of Canada, Cluster Sampling, Confidence Interval

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Published on 13 Apr 2013
School
U of S
Department
Sociology
Course
SOC 232
Professor
Page:
of 3
Soc 232
March 20th 2013
1
Example of multi-stage cluster sampling
Randomly select clusters, e.g. five provinces or territories from a list of all
provinces and territories.
Randomly select a number of subunits from each cluster (province/territory) e.g.
census districts.
From each census district selected, randomly select census subdivisions.
Randomly select a certain number of residential blocks from each census
subdivision selected.
Randomly select a number of households from each block selected.
Randomly select a person to be included in the study from each selected
household.
Cluster samples are usually stratified as well, i.e. divided into strata or subgroups
before the clusters or subunits are selected .
In our example, to ensure regional representation, the provinces and territories
might be categorized into regions: BC, Prairies, Ontario, Quebec, Atlantic
provinces, and the northern territories.
A certain number of provinces or territories could be randomly selected from each
region that contains more than one province or territory.
Then the next steps would be taken.
Qualities of a probability sample
representative - allows for generalization from sample to population.
inferential statistical tests.
sample means can be used to estimate population means.
standard error (SE): estimate of discrepancy between sample mean and
population mean.
95% of sample means fall between +/- 1.96 SE from population mean.
Sampling Error
Probability samples with sufficient sample sizes minimize the amount of sampling
error, but some sampling error is bound to occur.
e.g. there is usually some difference between a sample mean and the
population mean (μ) that it is designed to represent.
About 95 per cent of all sample means lie within 1.96 standard errors of the mean.
Hence this range is called a confidence interval, a 95% confidence interval.
Sample Size
The absolute size of the sample matters.
(not the proportion of the population that it comprises.)
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As sample size increases, sampling error tends to decrease.
Common sample sizes:100, 400, 900, 1600, 2500.
Each size increase cuts the sampling error by 1/2, then 1/3, then 1/4, and then 1/5
respectively.
The biggest change occurs between 100 and 400.
Is an increased sample size worth the time and effort?
Often sample size is dictated by financial concerns.
Non-response
The response rate is the percentage of the sample that participates in the study.
Is there some particular issue common to the non-responders that brings them to
differ in some important way from those who participate?
Heterogeneity of the Population
Generally, the greater the heterogeneity of the population on the characteristics
of interest, the larger the sample size should be.
Kind of Analysis
The sample size needed may vary depending on what sort of analysis will be
done.
Types of Non-probability Sampling
Convenience sampling
Cases are included because they are readily available.
e.g. one could go to a mall and administer a survey to anyone willing to take part.
Snowball sampling: a form of convenience sampling. The researcher makes
contact with some individuals, who in turn provide contacts for other participants.
Quota sampling: Collecting a specified number of cases in particular categories to
match the proportion of cases in that category in the population. e.g. quotas of
people in certain groups such as age, gender, ethnicity, class, etc. Random
methods are NOT used to fill the quotas. Is used a lot in market research, but is
rarely used in social scientific research in North America.
Criticisms:
Quota samples are not likely to be representative.
Judgements about eligibility may be incorrect.
e.g. a researcher may misjudge a person’s age and mistakenly not include
the person.
The data gathered cannot be used to calculate inferential statistics (based
on random sampling).
Strengths of quota sampling:
Cheaper, and easier to manage compared to random sampling.
Can be conducted much more quickly than random sampling.
Good for pilot tests, exploratory research.
Sampling in Structured Observation
Often no sampling frame
e.g. a list of all people who were admitted to the emergency room at a
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particular hospital.
May involve time sampling
e.g. an emergency room may be observed at random times throughout the
day.
May include place sampling
e.g. a study of student activities on campus may involve a sampling of
places such as dining halls, pubs, classrooms, etc.
May include behaviour sampling
e.g., a researcher may want to observe every fifth interaction between
students and librarians at a particular reference desk.
Qualitative Sampling
In ethnography, convenience sampling and snowball sampling are commonly
used.
Some qualitative researchers engage in theoretical sampling:
Data are simultaneously collected and analyzed.
Data collection is determined by whatever theoretical or conceptual issues
emerge as the study progresses.
In addition to people, times and contexts may be sampled in qualitative studies.
e.g. if observing a biker gang, different times of the day should be used, as well as
different contexts, such as those involving the presence of rival gang members,
the presence of law enforcement officers, etc.
Content Analysis Sampling
Media may be sampled:
e.g. a study of newspaper articles may involve sampling of different papers, of
articles on a given topic, etc.
Dates may be sampled:
e.g. if researching media portrayals of prostitutes, one could use a random
method to select the years for which the media are to be analyzed.
Reducing Non-response
Call backs are useful.
Sometimes several are necessary.
They reassure prospective participants that you are serious about the research.
Dress appropriately for face-to-face contact.
Be flexible to accommodate participants.

## Document Summary

Randomly select clusters, e. g. five provinces or territories from a list of all provinces and territories. Randomly select a number of subunits from each cluster (province/territory) e. g. census districts. From each census district selected, randomly select census subdivisions. Randomly select a certain number of residential blocks from each census subdivision selected. Randomly select a number of households from each block selected. Randomly select a person to be included in the study from each selected household. Cluster samples are usually stratified as well, i. e. divided into strata or subgroups before the clusters or subunits are selected . In our example, to ensure regional representation, the provinces and territories might be categorized into regions: bc, prairies, ontario, quebec, atlantic provinces, and the northern territories. A certain number of provinces or territories could be randomly selected from each region that contains more than one province or territory.