Sampling Techniques

Governments, companies, and news agencies often want to know the public’s opinion on pertinent questions.

Elections offer an excellent example of sampling and bias.

Suppose you want to know who is going to win the next election?

Clearly it is not feasible to ask every person in the country directly.

You can probably get an idea of the results by asking only a certain number of people…

The question is, “how many?”

A marketing research firm (Ipsos-Reid or Ekos or Decima)

would be hired by a news agency (CBC) to poll the public…

Record the final results of our last federal election by clicking below

Examine the following

Check the following website to see how the polls were able to

track and predict the resultsThe dates of each collection are

on the x axis

results

A private company must be efficient to stay in business.

If a company asks too many people,

they are wasting time and money

If a company asks too few people,

the results will not be valid.

Determining the right number of respondents is a major challenge to these companies

Canada’s population is about 32.5 million

There are about 22.5 million registered voters

Approximately 60% of the registered voters actually vote

About 13.5 million people vote

Canada’s population is about 32.5 million

There are about 22.5 millions registered voters

Approximately 60% of the population actually votes

About 13.5 million people vote

SES polls tracks 1200 voters

0.0089% of the population !!!!!!

Population

• All individuals in the group being studied

Sample

• A subset of the population

To see some examples of samples taken from

populations, check out the website below

samples

There are a number of different ways populations can be

sampled.

Simple Random Sample

All selections must be independent of one another and equally likely

Use a random number generator, dice, or a hat draw to ensure the data is randomly sampled.

Systematic Random Sample

Used when you are sampling a fixed percent of the population.

A random starting point is chosen, and then you select every nth individual, where n is the sampling interval.

For example

You want to determine the height of 25% of the students in this class. (9 out of 36)

369

= 4

The sampling interval would be 4

Randomly select the first person to measure (from 1 to 4), then measure every 4th person after them.

Stratified Random SamplingThe population is divided into

different groups called strata (ex. geographic areas, gender,age).

A simple random sample of the members in each stratum is taken.

The size of the sample is proportional to the stratum’s size. (a consistent percent)

Other sampling techniques

Make a note of the sampling techniques discussed on page 116 in the text.

Sampling Summary Chart

Simple Random Sample Every member of the population has an equal and independent chance of being selected

Systematic Sample Select the members at regular intervals starting from a random spot

Stratified Sample Divide the population into strata that have something in common (age, province…). Select a SRS from each strata

Cluster Sample Certain groups can be sampled if they represent the entire population. All the employees at a single McDonalds.

Multi-Stage Sample Two or more SRSs. Cities, then subdivisions, then houses.

Voluntary Response Collect data on a voluntary basis. ie: call in show or mail in survey

Convenience Sample The sample is selected because it is easily accessible. Not as random as other techniques.

Page 117

1,2,4,8,9

Plus examples on pg 116