Sample Size Calculations for Population Size Estimation Studies Using Multiplier Methods With Respondent-Driven Sampling Surveys

Fearon, Elizabeth, Chabata, Sungai, Thompson, Jennifer A, Cowan, Frances ORCID: https://orcid.org/0000-0003-3087-4422 and Hargreaves, James R (2017) 'Sample Size Calculations for Population Size Estimation Studies Using Multiplier Methods With Respondent-Driven Sampling Surveys'. JMIR Public Health and Surveillance, Vol 3, Issue 3, e59.

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Abstract

Background: While guidance exists for obtaining population size estimates using multiplier methods with respondent-driven sampling surveys, we lack specific guidance for making sample size decisions.

Objective: To guide the design of multiplier method population size estimation studies using respondent-driven sampling surveys to reduce the random error around the estimate obtained.

Methods: The population size estimate is obtained by dividing the number of individuals receiving a service or the number of unique objects distributed (M) by the proportion of individuals in a representative survey who report receipt of the service or object (P). We have developed an approach to sample size calculation, interpreting methods to estimate the variance around estimates obtained using multiplier methods in conjunction with research into design effects and respondent-driven sampling. We describe an application to estimate the number of female sex workers in Harare, Zimbabwe.

Results: There is high variance in estimates. Random error around the size estimate reflects uncertainty from M and P, particularly when the estimate of P in the respondent-driven sampling survey is low. As expected, sample size requirements are higher when the design effect of the survey is assumed to be greater.

Conclusions: We suggest a method for investigating the effects of sample size on the precision of a population size estimate obtained using multipler methods and respondent-driven sampling. Uncertainty in the size estimate is high, particularly when P is small, so balancing against other potential sources of bias, we advise researchers to consider longer service attendance reference periods and to distribute more unique objects, which is likely to result in a higher estimate of P in the respondent-driven sampling survey.

Item Type:	Article
Subjects:	QS Anatomy > QS 4 General works. Classify here works on regional anatomy WA Public Health > WA 30 Socioeconomic factors in public health (General) WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries WA Public Health > Statistics. Surveys > WA 900 Public health statistics WA Public Health > Statistics. Surveys > WA 950 Theory or methods of medical statistics. Epidemiologic methods WB Practice of Medicine > Diagnosis > General Diagnosis > WB 293 Collections of clinical case reports WC Communicable Diseases > Sexually Transmitted Diseases > WC 140 Sexually transmitted diseases
Faculty: Department:	Clinical Sciences & International Health > International Public Health Department
Digital Object Identifer (DOI):	https://doi.org/10.2196/publichealth.7909
Depositing User:	Stacy Murtagh
Date Deposited:	19 Sep 2017 11:06
Last Modified:	25 Sep 2017 09:18
URI:	https://archive.lstmed.ac.uk/id/eprint/7600

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