Sampling Methods: How to collect data Some important terms Random - - PowerPoint PPT Presentation

Sampling Methods: How to collect data

Some important terms

Random - occurring by chance Population – a group of individuals or

items that a study focuses on

Sample – a subset of the population, i.e.

individuals selected for the study….Why do we need a sample?

Samples are important because…

It’s much cheaper to collect data from

a subset of a population than the whole population.

It also costs less in terms of resources

(person-power, computer-power, paper, etc.) to collect data from a subset of a population than the whole population.

It’s also more efficient in terms of

time to collect data from a sample.

Simple Random Sampling

all selections must be equally likely all combinations of selections must be

equally likely

A random sample may not end up being

representative of the population, but any deviations are only due to chance. Much like in probability, even though something is very unlikely to happen, it still may happen by chance.

Simple Random Sampling approach

• A yearbook survey at

CB

• population: the

students of CB

• Sample size required:

100

• get a list of all 1100

students at CB and number them

• use a spreadsheet to

generate 100 random integers from 1 to 1100

• if any number appeared

more than once, we would have to generate a new number, i.e. you can't survey the same person more than once

Advantages of Simple Random Sampling

This is the simplest method to carry

• ut.

This method will most likely generate

the most random sample.

Disadvantages of Simple Random Sampling

This is the costliest method to carry

• ut in terms of resources and $$$.

Systematic Random Sampling

you sample a fixed percent of the

population

randomly choose a starting point then sample every nth individual, where

size sample size population n = = = =

Systematic Sampling approach

• A yearbook survey at CB
• population: the students of

CB

• Sample size required: 100

100 1100 = n

• n is 11, so we can choose to survey

every 11th person until we reach 100 surveyed

• Use our list of 1100 students and

generate one random integer (i) from 1 to 1100 a spreadsheet. That number will represent the first person we survey. Suppose i = 397, then start at element 397 and count 11 from them and survey that

• person. Continue in this way until

you have the sample size you need.

• If you get to the end of the list,

continue counting at the beginning.

Advantages of Systematic Random Sampling

This method will work very well any time your

population is in a line, listed somehow, or one element is arriving one after the next.

This a very simple and inexpensive method to

carry out if your population is in a line in front of you (e.g. a line up of people waiting to see Star Wars VII).

Disadvantages of Systematic Random Sampling

• This method requires a lot of resources if your

population is very large (like thousands or millions of elements). It will take a looooong time to count through the list to get your sample.

• This method is very difficult to carry out if the

population is not listed or lined up.

• This method will be very expensive if your elements

are very spread out. For example, suppose you want to personally interview a sample of people from the J.K. Rowling fan writing club. You have a list of world wide members, and select 100 of them. You have to fly all over the world to interview them. $$$!

Stratified Random Sampling

divide population into groups called

strata (maybe by age, location, etc.)

a simple random sample of each strata

is conducted

the size of the sample is proportional

to the size of the strata

Stratified Sampling approach

• A yearbook survey at CB
• population: the students of

CB

• Sample size required: 100
• strata will be grades 9, 10 ,

11, and 12

• calculate the percentage of

the students in each grade, that will tell you how many students to survey from each grade since our sample size is 100

• get a list of students by

grade and use a spreadsheet to pick students from each of the grades depending on how may students are in that grade

Advantages of Stratified Random Sampling

This method will ensure that every

subset (of interest) of the population is represented.

Because each subset is sampled

proportionally, an overall average or

• pinion can be determined.

Disadvantages of Stratified Random Sampling

This requires a lot of resources! This method generates different sized

subsets, so you have to be very careful when comparing them. You must compare PROPORTIONALLY!!!

Cluster Random Sampling

• rganize the population into groups

randomly select groups select all people in the selected groups

Cluster Sampling approach

A yearbook survey

at CB

population: the

students of CB

Sample size

required: 100

• group by first period

class

• randomly select 3 or 4

classes and survey everyone in each of those classes to do the survey to get the required 100 surveys

Advantages of Cluster Random Sampling

This method requires the least amount

• f $$$, time, and resources. Imagine

researchers are surveying Inuit

• populations. The researchers wouldn’t

have to travel to every single town. They can choose a small subset to visit.

Disadvantages of Cluster Random Sampling

This method introduces bias into the

• survey. Because only a small number of

groups of the population are surveyed

• r tested, only those opinions are

represented.

Multistage Random Sampling

• rganize the population into groups

randomly select groups randomly sample individuals in the

selected groups

• This method is called “multi”stage because the

researcher must generate “multi” random samples. The first is the random sample from the groups, then the researcher must create a random sample for each group chosen.

Multi-Stage Sampling approach

A yearbook survey

at CB

population: the

students of CB

Sample size

required: 100

group by first

period class

randomly select 10

first period classes

randomly select 10

students from each

• f those 10 classes

to complete the survey

Advantages of Multistage Random Sampling

This method is fairly efficient,

especially when data is very spread out. For example, if a researchers are surveying Inuit populations, they don’t have to travel to every single town. They can choose a subset to visit.

More groups are surveyed compared to

cluster, so there will be less bias.

Disadvantages of Cluster Random Sampling

This method, like cluster random sampling,

introduces bias into the survey. Because only a small number of subsets of the population are surveyed or tested, only those opinions are represented. Less bias is introduced, however, since more groups are surveyed.

It will be more expensive than cluster

random sampling, since more groups are being surveyed.

Destructive Sampling

This is simple or systematic random

sampling where selected items cannot be reintroduced into the population. They are destroyed either as a result

• f the testing or after they are tested.

Example: Light bulbs are being tested

for quality control. After a bulb is tested it cannot be sold so it is removed from the population.

Advantages of Destructive Random Sampling

This method allows companies to test

their product for quality control. This gives their consumers confidence in the product, allows the company to improve their product, and limits the company’s liability for defective parts.

Disadvantages of Destructive Random Sampling

This method decreases the amount of

product in circulation, depending on how many elements are tested. It costs the company money to perform the test, and it costs the company money because they are destroying product.

Do you get it?????

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