An Optimum Sample Size in Cross Sectional Studies

J. Sreedharan¹ , S. Chandrasekharan² , A. Gopakumar³

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.1 , pp.122-130, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i1.122130

Online published on Feb 28, 2019

Copyright © J. Sreedharan, S. Chandrasekharan, A. Gopakumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Sample size plays a vital role in any research as very less sample and very large sample may lead to false conclusions. There are many formulas available for the calculation of sample size according to the design of the study or/and statistical tool. These formulas generally give a minimum sample size but not provide solution for finding the required upper limit. Studies which include large sample give estimates with higher precision but may lead to large sample fallacy with statistical significance even for insignificant effects. This paper aimed to develop a formula for optimum sample size in the context of cross sectional study design. Pattern of changes in the results at minimum, optimum, large and extreme large sample size is discussed. Also we compare the result of cross sectional study with the result of a case control study. This study observed that an additional factor (m-factor) to be multiplied with the existing formula, that gives an optimum large sample in a cross-sectional study. The m-factor is a function of Zα and Zβ.

Key-Words / Index Term :
Minimum sample size, Optimum sample size, Large sample size, Cross sectional study, Power

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