| 5. Sampling and estimation in hidden populations using respondent-driven sampling MJ Salganik, DD Heckathorn Sociological methodology 34 (1), 193-240, 2004 | 2791 | 2004 |
| Experimental study of inequality and unpredictability in an artificial cultural market MJ Salganik, PS Dodds, DJ Watts Science 311 (5762), 854-856, 2006 | 2733 | 2006 |
| Bit by bit: social research in the digital age MJ Salganik Princeton University Press, 2018 | 1206 | 2018 |
| Variance estimation, design effects, and sample size calculations for respondent-driven sampling MJ Salganik Journal of Urban Health 83 (Suppl 1), 98-112, 2006 | 571 | 2006 |
| Assessing respondent-driven sampling S Goel, MJ Salganik Proceedings of the National Academy of Sciences 107 (15), 6743-6747, 2010 | 460 | 2010 |
| Computational social science: Obstacles and opportunities DMJ Lazer, A Pentland, DJ Watts, S Aral, S Athey, N Contractor, ... Science 369 (6507), 1060-1062, 2020 | 436 | 2020 |
| Leading the herd astray: An experimental study of self-fulfilling prophecies in an artificial cultural market MJ Salganik, DJ Watts Social psychology quarterly 71 (4), 338-355, 2008 | 430 | 2008 |
| Measuring the predictability of life outcomes with a scientific mass collaboration MJ Salganik, I Lundberg, AT Kindel, CE Ahearn, K Al-Ghoneim, ... Proceedings of the National Academy of Sciences 117 (15), 8398-8403, 2020 | 309 | 2020 |
| Integrating explanation and prediction in computational social science JM Hofman, DJ Watts, S Athey, F Garip, TL Griffiths, J Kleinberg, ... Nature 595 (7866), 181-188, 2021 | 290 | 2021 |
| Diagnostics for respondent-driven sampling KJ Gile, LG Johnston, MJ Salganik Journal of the Royal Statistical Society Series A: Statistics in Society 178 …, 2015 | 283 | 2015 |
| Strengthening the reporting of observational studies in epidemiology for respondent-driven sampling studies:“STROBE-RDS” statement RG White, AJ Hakim, MJ Salganik, MW Spiller, LG Johnston, L Kerr, ... Journal of clinical epidemiology 68 (12), 1463-1471, 2015 | 265 | 2015 |
| How many people do you know in prison? Using overdispersion in count data to estimate social structure in networks T Zheng, MJ Salganik, A Gelman Journal of the American Statistical Association 101 (474), 409-423, 2006 | 264 | 2006 |
| Respondent‐driven sampling as Markov chain Monte Carlo S Goel, MJ Salganik Statistics in medicine 28 (17), 2202-2229, 2009 | 254 | 2009 |
| How many people do you know?: Efficiently estimating personal network size TH McCormick, MJ Salganik, T Zheng Journal of the American Statistical Association 105 (489), 59-70, 2010 | 248 | 2010 |
| Wiki surveys: Open and quantifiable social data collection MJ Salganik, KEC Levy PLoS ONE 10 (5), e0123483, 2015 | 214 | 2015 |
| Counting hard-to-count populations: the network scale-up method for public health HR Bernard, T Hallett, A Iovita, EC Johnsen, R Lyerla, C McCarty, M Mahy, ... Sexually transmitted infections 86 (Suppl 2), ii11-ii15, 2010 | 205 | 2010 |
| Web‐based experiments for the study of collective social dynamics in cultural markets MJ Salganik, DJ Watts Topics in cognitive science 1 (3), 439-468, 2009 | 199 | 2009 |
| Assessing network scale-up estimates for groups most at risk of HIV/AIDS: evidence from a multiple-method study of heavy drug users in Curitiba, Brazil MJ Salganik, D Fazito, N Bertoni, AH Abdo, MB Mello, FI Bastos American journal of epidemiology 174 (10), 1190-1196, 2011 | 132 | 2011 |
| Commentary: respondent-driven sampling in the real world MJ Salganik Epidemiology 23 (1), 148-150, 2012 | 98 | 2012 |
| Generalizing the network scale-up method: a new estimator for the size of hidden populations DM Feehan, MJ Salganik Sociological methodology 46 (1), 153-186, 2016 | 89 | 2016 |
