Show Me the Literature on Mobile Data Collection!

Posted by MohiniBhavsar on Aug 31, 2010

One of the the key functions of mobile phones is their use in data collection. We have seen lots of online discussion here at and elsewhere on the subject.

Here, we feature a peer-reviewed journal article from our growing list of resources on mobile data collection. In this 2009 paper, Ping et al. evaluated the effectiveness of PDA-based questionnaire verses a paper-based method for public health surveillance in Fiji.

The authors showed that the gains in data accuracy using PDA technology were due to software that can automatically check for inconsistencies, missing values and skip logic at the time of data collection. But most impressive is their comparison of cost and time savings at each stage of project implementation – pre-deployment, data collection, and data entry, validation and cleaning stage. Though, there are some limitations in their study design (described in the slidecast), this paper is a good example of some parameters that should be captured in evaluations of mobile data collection projects.

Citation: Yu, P. et al. (2009). The development and evaluation of a PDA-based method for public health surveillance data collection in developing countries, Int. J. Med. Inform. 78(8):532-42

New Mobile Data Resource Coming Soon!

Recently, we collaborated with UN Global Pulse to crowd source mobile data collection deployments around the world. The inventory can be accessed here: To supplement this inventory, we are collecting all literature relevant to evaluations of mobile data collection projects and will soon share this (giant) round-up of blog posts, peer-reviewed research, evaluations and technical reports, case studies and How-Tos. We hope this compilation of resources will directly inform practitioners, who are looking to set up similar field projects.

Some Early Observations of the Existing Literature

Of the literature that exists, much of it does not adequately discuss evaluative metrics for mobiles in data collection projects. Comprehensive evaluation and monitoring of pilots is important in order for practitioners to understand the value-add of mobiles and feasibility of scale-up. The majority of evaluations we have seen that are focused on mobiles in data collection, report on savings in costs and time but do not break down these savings in detail.  Additionally, the discussion on effectiveness of mobiles is largely qualitative. Interviews and user perceptions are very useful for understanding local contexts and usability of technology. But to convince organizations to adopt mobile technology in their work, quantitative metrics should be presented as well.

Listed below are some parameters that could be measured. This list is not exhaustive, but a reflection of the taxonomy used in existing literature that should be built on:

  • reach of target group (as compared to existing methods)
  • usability and user perceptions (data collectors, participants, managers etc.)
  • effect on management of staff (e.g. productivity)
  • accuracy and quality of data collected (compared to existing methods)
  • ease and extent of data capture (compared to other methods)
  • effect on work flow management
  • time and costs of training (initial and on-going, in-person support, and other resources)
  • initial purchasing costs of technology and on-going (estimated) costs
  • costs associated with evaluation and/or development of technology
  • ease and cost of data transmission (based on network connectivity etc.)
  • level of participation and user-input
  • data security and protection (increasing a concern with data collection projects for health)
  • localization/customization/adaptability of software/hardware
  • technical capacity (pre-deployment and on-going)

Including the above, longer term evaluations, should also assess how mobile data collection efforts have improved the deliverables of the project, informed policy, or increased public awareness. A more standardized (or to start, a more comprehensive) approach for the monitoring and evaluation could better inform practitioners of region-specific best practices, lessons learned, barriers and challenges that may arise.

Show Me the Literature on Mobile Data Collection! data sheet 3207 Views
Countries: Fiji

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