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

28 views3 pages

Soc 232

March 20th 2013

1

Example of multi-stage cluster sampling

Random Sample of Canadian Adult Population:

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.)

Soc 232

March 20th 2013

2

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

Soc 232

March 20th 2013

3

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.