Why would we only consider random sampling to have a sampling frame (considering the entire population) ? Could you please elaborate on this concept ? Do you mean by it that we give every individual an equal chance to be selected, and therefore everyone is fitted within the frame of selection, and hence the “sampling frame”. And therefore other sampling methods’ population cannot be given the title of “sampling frame”. Please confirm or correct elaborately if possible.

Do you mean by it that we give every individual in the population an equal chance to be sampled, and therefore the population is called the sampling frame. Otherwise if it was just about the necessity of having knowledge of each item in the population, then the population of Stratified sampling for instance should also qualify being called as a “sampling frame”.

There is no need for a lecture – what is written in the free lecture notes is all that is needed for the exam (there is not much point in me just reading it out when you can read it yourself 🙂 ).

If you are not clear about what is written then ask in the Ask the Tutor Forum and I will explain the bit that you are not clear about.

Thanks a lot, Your act is very selfless, generous and kind. You are helping thousands of student who can’t afford the costly books published by theBPP/KAPLAN. You are the only hope of those people. You are the short cut for people who don’t want to go through the 600 pages of Kaplan or 400 pages of Bpp or stuck at somewhere. I appreciate your Such a selfless and kindful act. What I think about you Sir, is that, you are one of those sent directly by God.

Once again thank you sir . you lectures are awesome.

I may be being silly, but I’m a little confused by the difference between stratified sampling and quota sampling.

The course notes explain that:

Stratified: If the population was 60% women, 40% men, then 60% of the sample should be women and 40% men.

Quota: Population is 60% women and 40% men. We want to question a sample of 200. Decide on a quota of 120 women (60%) and 80 men(40%), and stop people as they appear until we have the required number.

The only difference I see is the way in which candidates are chosen (one by one until the quota is satisfied), however, in stratified sampling, it makes no reference to how the 60% and respective 40% are selected.

I think – In Stratified sampling – The composition/categorisation Sampling should be equal to the composition/categorisation of whole population.

e.g real populayion using the product is 60% women and 40 % men , then it doesn’t matter how much sample we take. its just whatever Qty of sample we take should have same composition ( like sample also should be 60 % women and 40% men )

while quata in sampling – They just keep doing sampling till they get the quata fullfilled.

Also, one thing to notice Quata is Not random at all , as we are not ramdomly selecting from the quota e.g we aren’t selecting 120 women randomly from 60 % of total women from a list – the original example )

while in stratified sampling which is quasi random . because we select randomly from 60 percent of women.

Say you are a business owner and you have 100 offices and each office has 100 employees. You need a random sample of employees.

Multi Stage: Select 5 offices at random and from each office you select 20 employees at random. So total So sample is 100 employees from 5 different offices.

Cluster: Select 5 offices at random and then use every single employee from those offices as your sample.

Asif110 says

Greetings sir,

Why would we only consider random sampling to have a sampling frame (considering the entire population) ? Could you please elaborate on this concept ? Do you mean by it that we give every individual an equal chance to be selected, and therefore everyone is fitted within the frame of selection, and hence the “sampling frame”. And therefore other sampling methods’ population cannot be given the title of “sampling frame”. Please confirm or correct elaborately if possible.

Asif110 says

Do you mean by it that we give every individual in the population an equal chance to be sampled, and therefore the population is called the sampling frame. Otherwise if it was just about the necessity of having knowledge of each item in the population, then the population of Stratified sampling for instance should also qualify being called as a “sampling frame”.

shoem says

Hi there! why i dont see the Big Data lecture?

farahn says

the exact question i wanted to ask?

John Moffat says

There is no need for a lecture – what is written in the free lecture notes is all that is needed for the exam (there is not much point in me just reading it out when you can read it yourself 🙂 ).

If you are not clear about what is written then ask in the Ask the Tutor Forum and I will explain the bit that you are not clear about.

lokeshdh00 says

Thanks a lot, Your act is very selfless, generous and kind. You are helping thousands of student who can’t afford the costly books published by theBPP/KAPLAN. You are the only hope of those people. You are the short cut for people who don’t want to go through the 600 pages of Kaplan or 400 pages of Bpp or stuck at somewhere.

I appreciate your Such a selfless and kindful act.

What I think about you Sir, is that, you are one of those sent directly by God.

Once again thank you sir . you lectures are awesome.

mahadosman says

wonderful lecturer thanks you teacher.

tuathanach says

Hi.

Thank you for the wonderful lectures.

I may be being silly, but I’m a little confused by the difference between stratified sampling and quota sampling.

The course notes explain that:

Stratified:

If the population was 60% women, 40% men, then 60% of the sample should be women and 40% men.

Quota:

Population is 60% women and 40% men. We want to question a sample of 200. Decide on a quota of 120 women (60%) and 80 men(40%), and stop people as they appear until we have the required number.

The only difference I see is the way in which candidates are chosen (one by one until the quota is satisfied), however, in stratified sampling, it makes no reference to how the 60% and respective 40% are selected.

Any help would be greatly appreciated.

Best,

Scott

lokeshdh00 says

I think – In Stratified sampling – The composition/categorisation Sampling should be equal to the composition/categorisation of whole population.

e.g real populayion using the product is 60% women and 40 % men , then it doesn’t matter how much sample we take. its just whatever Qty of sample we take should have same composition ( like sample also should be 60 % women and 40% men )

while quata in sampling – They just keep doing sampling till they get the quata fullfilled.

Also, one thing to notice Quata is Not random at all , as we are not ramdomly selecting from the quota e.g we aren’t selecting 120 women randomly from 60 % of total women from a list – the original example )

while in stratified sampling which is quasi random . because we select randomly from 60 percent of women.

ritaporter says

Hi There, do you cover the Big Data topic?

briannyangena says

Wonderful lectures.

keep up with the spirit

John Moffat says

Thank you for your comment 🙂

kodi1122 says

Can you kindly explain Multistage and Cluster sampling more vividly I’m still not getting it, other than that the video is indeed helpful.

hma1989 says

Say you are a business owner and you have 100 offices and each office has 100 employees. You need a random sample of employees.

Multi Stage: Select 5 offices at random and from each office you select 20 employees at random. So total So sample is 100 employees from 5 different offices.

Cluster: Select 5 offices at random and then use every single employee from those offices as your sample.

darinstephen says

Explained alot. Thnks!

anasci83 says

Wow these videos are actually much easier to understand than the lectures I’m attending to. Thanks

John Moffat says

Thank you for your comment 🙂

agbamoroo says

wonderful

John Moffat says

Thank you for the comment 🙂

gk77 says

very usefull.

John Moffat says

Thank you for your comment 🙂