2016•IRA-International Journal of Applied Sciences (ISSN 2455-4499)Open access

Sample Size Estimation and Power Analysis for Research Studies Using R

A P Suma, KP Suresh

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Abstract

Sample size estimation is very crucial in any research design. A research design with less sample size may give a biased result or inconclusive result. A research design with very large sample size than required results is waste of resources, time and energy. So, it is very essential to determine ‘ideal’ or ‘optimum’ sample size. This article gives formulae and R code for determining sample size for single mean, two means, single proportion, two proportions, proportion in survey type data, case control studies, cohort studies, correlation coefficient and difference between correlation coefficients.

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Sample size estimation is very crucial in any research design. A research design with less sample size may give a biased result or inconclusive result. A research design with very large sample size than required results is waste of resources, time and energy. So, it is very essential to determine ‘ideal’ or ‘optimum’ sample size. This article gives formulae and R code for determining sample size for single mean, two means, single proportion, two proportions, proportion in survey type data, case control studies, cohort studies, correlation coefficient and difference between correlation coefficients.

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Available abstract

Sample size estimation is very crucial in any research design. A research design with less sample size may give a biased result or inconclusive result. A research design with very large sample size than required results is waste of resources, time and energy. So, it is very essential to determine ‘ideal’ or ‘optimum’ sample size. This article gives formulae and R code for determining sample size for single mean, two means, single proportion, two proportions, proportion in survey type data, case control studies, cohort studies, correlation coefficient and difference between correlation coefficients.

Key concepts: Sample size determination, Statistics, Sample (material), Mathematics, Estimation, Correlation, Econometrics, Engineering

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