TY  - JOUR
AU  - Fearon, Elizabeth
AU  - Chabata, Sungai T
AU  - Thompson, Jennifer A
AU  - Cowan, Frances M
AU  - Hargreaves, James R
PY  - 2017
DA  - 2017/09/14
TI  - Sample Size Calculations for Population Size Estimation Studies Using Multiplier Methods With Respondent-Driven Sampling Surveys
JO  - JMIR Public Health Surveill
SP  - e59
VL  - 3
IS  - 3
KW  - population surveillance
KW  - sample size
KW  - sampling studies
KW  - surveys and questionnaires
KW  - research design
KW  - data collection
KW  - sex workers
KW  - HIV
AB  - 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. 
SN  - 2369-2960
UR  - http://publichealth.jmir.org/2017/3/e59/
UR  - https://doi.org/10.2196/publichealth.7909
UR  - http://www.ncbi.nlm.nih.gov/pubmed/28912117
DO  - 10.2196/publichealth.7909
ID  - info:doi/10.2196/publichealth.7909
ER  -