Tweeted twitter.com/#!/StackStats/status/266162513530458112
4 Changed some notation to match with traditional notation in sampling. Add the name of the sample allocation mentioned in the question.
source | link

In stratified sampling, what are the optimization considerations? For example, the number of samplessample size per stratum could be defined with proportional allocation as $s_h=\frac{n_h}{N}*s$$n_h=n\frac{N_h}{N}$, where $n_h$$N_h$ is the population size for stratum h$h$, $N$ is the total population size, $s$$n$ is the total sample size, and $s_h$$n_h$ is the sample size for stratum h. $h$.

For a binary variable, I can choose $s$$n$ so that my margin of error $e$ is 1%, 5% or 10%. $$e=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s}}$$$$e=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{n}}$$

I could also choose my $s$$n = \sum_{h=1}^H n_h$ such that the margin of error for each stratum $e_h$ is 1%, 5% or 10%. $$e_h=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s_h}}$$$$e_h=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{n_h}}$$

But my question/concern is, how many different considerations are there and which ones are the most important ones in the context of stratified sampling?

In stratified sampling, what are the optimization considerations? For example, the number of samples per stratum could be defined as $s_h=\frac{n_h}{N}*s$, where $n_h$ is the population size for stratum h, $N$ is the total population size, $s$ is the total sample size, and $s_h$ is the sample size for stratum h.

For a binary variable, I can choose $s$ so that my margin of error $e$ is 1%, 5% or 10%. $$e=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s}}$$

I could also choose my $s$ such that the margin of error for each stratum $e_h$ is 1%, 5% or 10%. $$e_h=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s_h}}$$

But my question/concern is, how many different considerations are there and which ones are the most important ones in the context of stratified sampling?

In stratified sampling, what are the optimization considerations? For example, the sample size per stratum could be defined with proportional allocation as $n_h=n\frac{N_h}{N}$, where $N_h$ is the population size for stratum $h$, $N$ is the total population size, $n$ is the total sample size, and $n_h$ is the sample size for stratum $h$.

For a binary variable, I can choose $n$ so that my margin of error $e$ is 1%, 5% or 10%. $$e=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{n}}$$

I could also choose my $n = \sum_{h=1}^H n_h$ such that the margin of error for each stratum $e_h$ is 1%, 5% or 10%. $$e_h=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{n_h}}$$

But my question/concern is, how many different considerations are there and which ones are the most important ones in the context of stratified sampling?

3 Fixed capitalization
source | link

inIn stratified sampling, what are the optimization considerations? forFor example, the number of samples per stratum could be defined as $s_h=\frac{n_h}{N}*s$, where $n_h$ is the population size for stratum h, $N$ is the total population size, $s$ is the total sample size, and $s_h$ is the sample size for stratum h.

forFor a binary variable, iI can choose $s$ so that my margin of error $e$ is 1%, 5% or 10%. $$e=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s}}$$

iI could also choose my $s$ such that the margin of error for each stratum $e_h$ is 1%, 5% or 10%. $$e_h=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s_h}}$$

butBut my question/concern is, how many different considerations are there and which ones are the most important ones in the context of stratified sampling?

in stratified sampling, what are the optimization considerations? for example, the number of samples per stratum could be defined as $s_h=\frac{n_h}{N}*s$, where $n_h$ is the population size for stratum h, $N$ is the total population size, $s$ is the total sample size, and $s_h$ is the sample size for stratum h.

for a binary variable, i can choose $s$ so that my margin of error $e$ is 1%, 5% or 10%. $$e=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s}}$$

i could also choose my $s$ such that the margin of error for each stratum $e_h$ is 1%, 5% or 10%. $$e_h=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s_h}}$$

but my question/concern is, how many different considerations are there and which ones are the most important ones in the context of stratified sampling?

In stratified sampling, what are the optimization considerations? For example, the number of samples per stratum could be defined as $s_h=\frac{n_h}{N}*s$, where $n_h$ is the population size for stratum h, $N$ is the total population size, $s$ is the total sample size, and $s_h$ is the sample size for stratum h.

For a binary variable, I can choose $s$ so that my margin of error $e$ is 1%, 5% or 10%. $$e=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s}}$$

I could also choose my $s$ such that the margin of error for each stratum $e_h$ is 1%, 5% or 10%. $$e_h=z_{1-\frac{\alpha}{2}} \sqrt{\frac{pq}{s_h}}$$

But my question/concern is, how many different considerations are there and which ones are the most important ones in the context of stratified sampling?

2 edited title
| link

what What are the optimizations or goals to consider when using stratified sampling?

1
source | link