A single weighting approach to analyze respondent-driven sampling data

Abstract

Background and objectives: Respondent-driven sampling (RDS) is widely used to sample hidden populations and RDS data are analyzed using specially designed RDS analysis tool (RDSAT). RDSAT estimates parameters such as proportions. Analysis with RDSAT requires separate weight assignment for individual variables even in a single individual; hence, regression analysis is a problem. RDS-analyst is another advanced software that can perform three methods of estimates, namely, successive sampling method, RDS I and RDS II. All of these are in the process of refinement and need special skill to perform analysis. We propose a simple approach to analyze RDS data for comprehensive statistical analysis using any standard statistical software. Methods: We proposed an approach (RDS-MOD - respondent driven sampling-modified) that determines a single normalized weight (similar to RDS II of Volz-Heckathorn) for each participant. This approach converts the RDS data into clustered data to account the pre-existing relationship between recruits and the recruiters. Further, Taylor's linearization method was proposed for calculating confidence intervals for the estimates. Generalized estimating equation approach was used for regression analysis and parameter estimates of different software were compared. Results: The parameter estimates such as proportions obtained by our approach were matched with those from currently available special software for RDS data. Interpretation & conclusions: The proposed weight was comparable to different weights generated by RDSAT. The estimates were comparable to that by RDS II approach. RDS-MOD provided an efficient and easy-to-use method of estimation and regression accounting inter-individual recruits' dependence.

Keywords

• New approach - regression - respondent-driven sampling- data analysis