
	Journal of Applied Sciences
	Year: 2017 \| Volume: 17 \| Issue: 4 \| Page No.: 196-203 DOI: 10.3923/jas.2017.196.203

Population Proportion Estimator of Respondent Driven Sampling for Non-dichotomous Variables, Data Smoothing Approach

Arezoo Bagheri

and Mahsa Saadati

Abstract: Background: Sampling and estimating of hidden population sizes, such as injection drug users are important issues for health policy makers, because of exposing these populations to high risks diseases, such as HIV/AIDS. Materials and Methods: Respondent driven sampling is a successful method in terms of resulting in representative sample of hidden populations and finding unbiased estimates comparing to the other existing conventional methods. Results: The main purpose of this study is to define population proportion estimation of this sampling method for dichotomous and non-dichotomous variables. For non-dichotomous variables, reciprocal approach results in over-determination equations which can be solved by either least squares or data smoothing approaches, though the late one is much more effective. A hypothetical data has been employed to find the estimation of dichotomous and non-dichotomous variables for respondent driven sampling method. Conclusion: The novelty of data smoothing procedure to find respondent driven sampling estimates has been proved by this hypothetical data. Respondent driven sampling method could result in unbiased estimates of population proportions and it has been recommended to be applied for studying hidden population proportions.

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How to cite this article:

Arezoo Bagheri and Mahsa Saadati, 2017. Population Proportion Estimator of Respondent Driven Sampling for Non-dichotomous Variables, Data Smoothing Approach. Journal of Applied Sciences, 17: 196-203.

DOI: 10.3923/jas.2017.196.203

URL: https://scialert.net/abstract/?doi=jas.2017.196.203

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