A NEW COMPRIMISE ALLOCATION METHOD IN STRATIFIED RANDOM SAMPLING

Year 2017, Volume: 30 Issue: 3, 181 - 194, 20.09.2017

Sinem Tuğba Şahin Tekin , Yaprak Arzu Özdemir , Cenker Burak Metin

Abstract

Sample
size of the strata is determined by the help of some allocation methods in
Stratified Random Sampling. Most of the allocation methods ignore the selection
cost. However, in real life applications it is very rare to come across such
situations. In this study, a new compromise allocation method is proposed by
adding a non-linear cost function constraint to Costa et al.(2004) method.
Using this new allocation, the sample size with linear cost constraint is also
obtained. The performance of the proposed method is studied utilizing the data
from Statistics Canada’s Monthly Retail Trade Survey (MRTS) of single
establishments used by Choudhry et al. (2012).

Keywords

Stratified Random Sampling, Compromise Allocation, Neyman Allocation, Optimum Allocation, Non-linear Cost Function

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