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 Examine the following

Check the following website to see how the polls were able to track and predict the results The 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 % 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 n th 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) 36 9 = 4 The sampling interval would be 4

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

Stratified Random Sampling The 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 SampleEvery member of the population has an equal and independent chance of being selected Systematic SampleSelect the members at regular intervals starting from a random spot Stratified SampleDivide the population into strata that have something in common (age, province…). Select a SRS from each strata

Cluster SampleCertain groups can be sampled if they represent the entire population. All the employees at a single McDonalds. Multi-Stage SampleTwo or more SRSs. Cities, then subdivisions, then houses. Voluntary ResponseCollect data on a voluntary basis. ie: call in show or mail in survey

Convenience SampleThe 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