|
Size: 1725
Comment:
|
← Revision 9 as of 2025-11-03 01:52:44 ⇥
Size: 0
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| = Survey Sampling = <<TableOfContents>> ---- == Sample Type == === Propability sampling === All members of a population have a non-zero chance to be contacted in a survey instrument. Traditional statistics rely on this assumption. Examples: * Administrative surveys * Surveys with random recruitment (as by random digit dialing) === Non-probability sampling === Some members of a population are certain to be contacted or not be contacted. Examples: * Panel surveys * River surveys (i.e. surveys with open recruitment, as by banner ads) ---- == Survey Allocation == Allocation is the distribution of sample size across domains. === Designing Domains === The key considerations are: * are some splits more important to others? * if studying military recruitment, then sex/gender is a strong split * what splits will be used for reporting? + what is the expected response rate? * if too few responses are expected from a domain, then splits should be reconsidered * what is the desired margin of error? === Stratified Allocation === Stratification is the process of dividing the population into discrete stratum, and then sampling from the strata. Within a stratified sample, allocation can be designed as: * equal from each stratum * proportional to the size of each stratum * an optimization of a key measure's margin of error against cost * if cost is assumed fixed per unit, it is a Neyman allocation * if cost is assumed variable, it is an optimal allocation * note that, if all measures are equally varied, proportional allocation is essentially the same as a Neyman allocation ---- CategoryRicottone |
