Why are multistage and cluster sampling often used together?

Title: SAMPLING
1. Why are multistation and gathering sampling frequently used coincidently?
Gathering sampling is the way used when proportionately contrasted barring unless collectionings of axioms are offer in a statistical population (Lohr and Sharon, 2010). This mold of sampling is for-the-most-part assiduous in traffic learning where the aggregate population is tamed into weak collections designated gatherings, and then the vague illustration is chosen (Thompson and Steven, 2012). On the other workman, multistation sampling is a contrive of sampling where sampling is carried extinguished in weaker sampling units than those of gathering sampling at each station. It is as-well a deep mold of gathering sampling gundivided it as-well involves infringement a population into collections (Levy et al. 2013). These span molds of sampling are in most cases used coincidently as the rule of sampling involves choosing undivided or past gatherings vaguely then each chosen collection is illustrationd. Gundivided using integral the illustrationd elements is dear, a association of gathering and multistation sampling designated multistation gathering sampling is assiduous by vaguely selecting items in each collection instead of using integral elements build in the chosen gathering.
2. What does having a assurance season of 99% average for the illustration?
When it is said that a illustration has a assurance flatten of 99%, it is implicit that undivided is 99% stable that the developed prize of a parameter is amid the cessation of faith. It expresses that 99% of the observed assurance cessations is mitigated to stop to the existent prize of the parameter (Barlow, 2013). Anytime a point illustration is smitten, the population parameter may either be build amid the collocate or referable attributable attributable. Having the observed axioms as vague illustrations from the developed population, makes the assurance cessation got from the axioms as-well vague. Therefore, having a flatten of faith of 99% averages that 99% of the cessations got from a illustration achieve entertain the existent parameter of the illustration. With the assurance flatten determined by the learninger and referable attributable attributable attributable the axioms substance used, then the measure of faith of the similar theory tests is the completion of the range of sentiment of a illustration axioms (Salkind et al. 2007).

References
Barlow, R. J. Statistics: A Guide to the Use of Statistical Ways in the Physical Sciences. Hoboken: Wiley, 2013. .
Levy, Paul S., and Stanley Lemeshow. Sampling of Populations: Ways and Applications. Hoboken, N.J.: Wiley, 2013. .
Lohr, Sharon L. Sampling: Design and Analysis. Mason, OH: South-Western Cengage Learning, 2010
Thompson, Steven K. Sampling. Hoboken, N.J.: Wiley, 2012.
Salkind, Neil J., and Kristin Rasmussen. Encyclopedia of Measurement and Statistics. Thousand Oaks, Calif: SAGE Socialations, 2007.

Author: Julie Green