Network Structure and Biased Variance Estimation in RDS
نویسندگان
چکیده
This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.
منابع مشابه
Network Structure and Biased Variance Estimation in Respondent Driven Sampling
This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of varianc...
متن کاملLinked Ego Networks: Improving estimate reliability and validity with respondent-driven sampling
Respondent-driven sampling (RDS) is currently widely used for the study of HIV/AIDS-related high risk populations. However, recent studies have shown that traditional RDS methods are likely to generate large variances and may be severely biased since the assumptions behind RDS are seldom fully met in real life. To improve estimation in RDS studies, we propose a new method to generate estimates ...
متن کاملEstimation of AR Parameters in the Presence of Additive Contamination in the Infinite Variance Case
If we try to estimate the parameters of the AR process {Xn} using the observed process {Xn+Zn} then these estimates will be badly biased and not consistent but we can minimize the damage using a robust estimation procedure such as GM-estimation. The question is does additive contamination affect estimates of “core” parameters in the infinite variance case to the same extent that it does in the ...
متن کاملMultiple seed structure and disconnected networks in respondent-driven sampling
Respondent-driven sampling (RDS) is a link-tracing sampling method that is especially suitable for sampling hidden populations. RDS combines an efficient snowball-type sampling scheme with inferential procedures that yield unbiased population estimates under some assumptions about the sampling procedure and population structure. Several seed individuals are typically used to initiate RDS recrui...
متن کاملAnalysis of data collected by RDS among sex workers in 10 Brazilian cities, 2009: estimation of the prevalence of HIV, variance, and design effect.
BACKGROUND Respondent-driven sampling (RDS) is a chain-referral method that is being widely used to recruit most at-risk populations. Because the method is respondent driven, observations are dependent. However, few publications have focused on methodological challenges in the analysis of data collected by RDS. METHODS In this article, we propose a method for estimating the variance of the HI...
متن کامل