Work Description

Title: Survey Distributional Methods Analyses Data Open Access Deposited
Attribute Value
  • The data used for this study comes from the survey responses of a study on Mobility-on-Demand in 2019. From the survey responses, we gathered the income information of the participants and the method through which they were recruited. From the study, we gathered the cost associated with each survey distributional method, and data such as number of people contacted, number of people responded, and number of complete/valid responses. We calculated a series of rates, including response rates, cost effectiveness, and average income through simple Excel functions, such as sums, additions, divisions, and means.
  • We compared the response rates, cost, and the average income of participants pertaining to 6 different survey distribution methods used in an initial study about mobility-on-demand services. We used the data to identify survey and recruitment methods that are more effective in reaching hard-to-reach populations. All the raw data used for calculations and the calculations themselves can be found in the attached spreadsheets.

  • Initial analyses have identified in-person onsite recruitment as one of the better methods of reaching hard-to-reach populations, and is calling for continued work in improving research methods in the field of HCI.
Contact information
Citations to related material
  • Yan, X., Zhao, X., Han, Y., and Hentenryck, P. V. (2019). Mobility-on-demand versus fixed-route transit systems: an evaluation of traveler preferences in low-income communities.
  • Atkinson, R., and Flint, J. Accessing Hidden and Hard-to-Reach Populations: Snowball Research Strategies. "Social Research Update" 33 (Jan 2001).
  • Buranyi, S. Rise of the racist robots: how ai is learning all our worst impulses, Aug 2017. Retrieved June 11, 2019 from
  • Dillahunt, T. R., Erete, S., Galusca, R., Israni, A., Nacu, D., and Sengers, P. Reflections on design methods for underserved communities. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (New York, NY, USA, 2017), CSCW ’17 Companion, ACM, pp. 409–413.
  • Erete, S., Israni, A., and Dillahunt, T. An intersectional approach to designing in the margins. Interactions 25, 3 (Apr. 2018), 66–69.
  • Foster, A. Concerning issue with driverless cars, Mar 2019. Retrieved June 11, 2019 from
  • Johnston, L. G., and Sabin, K. Sampling hard-to-reach populations with respondent driven sampling. Methodological Innovations Online 5, 2 (aug 2010), 38.1–48.
  • Macaulay, A. C., Commanda, L. E., Freeman, W. L., Gibson, N., McCabe, M. L., Robbins, C. M., and Twohig, P. L. Participatory research maximises community and lay involvement. BMJ 319, 7212 (sep 1999), 774–778.
  • Maestre, J. F., Eikey, E. V., Warner, M., Yarosh, S., Pater, J., Jacobs, M., Marcu, G., and Shih, P. C. Conducting research with stigmatized populations: Practices, challenges, and lessons learned. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (2018), ACM, pp. 385–392.
  • Paterson, J. M., and Maker, Y. Why does artificial intelligence discriminate?, Jun 2019. Retrieved June 11, 2019 from
  • Strohmayer, A., Laing, M., and Comber, R. Technologies and social justice outcomes in sex work charities: fighting stigma, saving lives. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (2017), ACM, pp. 3352–3364.
  • Sydor, A. Conducting research into hidden or hard-to-reach populations. Nurse researcher 20, 3 (2013).
Resource type
Curation notes
  • On August 21, 2019, metadata was updated with additional creators and citations to related material.
Last modified
  • 08/21/2019
  • 07/16/2019
To Cite this Work:
Xu, X., Yan, X., Dillahunt, T. (2019). Survey Distributional Methods Analyses Data [Data set]. University of Michigan - Deep Blue.


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