• Publications
  • Influence
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Hit songs, books, and movies are many times more successful than average, suggesting that “the best” alternatives are qualitatively different from “the rest”; yet experts routinely fail to predictExpand
  • 1,545
  • 85
  • PDF
5. Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
TLDR
We develop a sampling and estimation technique called respondent-driven sampling, which allows researchers to make asymptotically unbiased estimates about the characteristics of hidden populations such as injection drug users, the homeless, and artists. Expand
  • 1,489
  • 67
  • PDF
Variance Estimation, Design Effects, and Sample Size Calculations for Respondent-Driven Sampling
TLDR
We present a bootstrap method for constructing confidence intervals around respondent-driven sampling estimates and demonstrate in simulations that it outperforms the naive method currently in use. Expand
  • 361
  • 26
  • PDF
Assessing respondent-driven sampling
TLDR
We investigate the performance of RDS by simulating sampling from 85 known, network populations. Expand
  • 314
  • 22
  • PDF
How Many People Do You Know in Prison?
Networks—sets of objects connected by relationships—are important in a number of fields. The study of networks has long been central to sociology, where researchers have attempted to understand theExpand
  • 155
  • 16
  • PDF
Bit by Bit: Social Research in the Digital Age
  • 143
  • 15
  • PDF
Respondent-driven sampling as Markov chain Monte Carlo.
TLDR
We present RDS as Markov chain Monte Carlo importance sampling, and we examine the effects of community structure and the recruitment procedure on the variance of RDS estimates. Expand
  • 169
  • 13
  • PDF
Diagnostics for Respondent-driven Sampling.
TLDR
Inference from RDS data requires many strong assumptions because the sampling design is partially beyond the control of the researcher. Expand
  • 148
  • 13
  • PDF
Counting hard-to-count populations: the network scale-up method for public health
Estimating sizes of hidden or hard-to-reach populations is an important problem in public health. For example, estimates of the sizes of populations at highest risk for HIV and AIDS are needed forExpand
  • 101
  • 9
  • PDF
How Many People Do You Know?: Efficiently Estimating Personal Network Size
TLDR
We develop a method to estimate both individual social network size (i.e., degree) and the distribution of network sizes in a population by asking respondents how many people they know in specific subpopulations. Expand
  • 126
  • 9
  • PDF