Four constraints for adoption of electronic data collection methods

Making informed policy decisions requires having access to high-quality data. In the last 15 years, technological advancements have led to a dramatic shift toward more efficient and reliable methods of collecting data. The development community and national statistics agencies utilize advanced computer-assisted technology for data collection. Despite its benefits, the adoption of electronic data collection tools remains a challenge in many low-income countries. 

Comparison of Commonly Used Data Collection Methods

The World Bank is actively promoting the adoption of modern data gathering methods to increase the quantity and quality of data accessible through many initiatives. These include: Development Data Group’s work with national statistics offices and Geo-Enabling Initiative for Monitoring and Supervision (GEMS) in fragile contexts. Members of the Development Impact Evaluation department (DIME) frequently train their government counterparts on data analytics.

Lessons from a Capacity Building Workshop in Ouagadougou

DIME recently trained 28 government officials in Ouagadougou, Burkina Faso, on how to collect high-quality data using computer-assisted data collection techniques. Specifically, the training provided: an overview of available survey technologies (such as paper, SMS, electronic and web-based surveys); the basics of programming and using an electronic questionnaire; and best practices to ensure data quality when preparing for data collection (all the materials are publicly available on Open Science Framework). But, three months after the training, only two trainees reported using computer-assisted surveys in their daily work.

What Prevents the Implementation of Computer-assisted Data Collection?

Based on class discussions and participants’ responses to a feedback survey, we have identified four main constraints: (1) choice of platform, (2) costs, (3) lack of capacity, and (4) accessibility.  

Choosing a platform

The choice of technology is often the first source of confusion for those transitioning from paper-based to computer-assisted data collection. The difficulty comes from having many options, which makes it hard to know where to begin  when it comes to investing in capacity-building. [For this training, we used SurveyCTO, a computer-assisted data collection platform based on Open Data Kit (ODK), a free and open-source software. Other examples include KoBoToolbox, ODK Aggregate, and ONA].

For help weighing the advantages and disadvantages of various ODK-based platforms, see this report by CartONG.

Circumventing costs

We decided to use ODK as the base technology for our training because the software is free, and the ODK-based platforms can do the back-end hosting for surveys. Although they are often not free, knowing ODK allows users to take advantage of the various pricing plans and features provided by these platforms. 

Logistics and cost of acquiring tablets were the second and third most mentioned constraints to adoption. However, once procured, tablets can be used multiple times and offset the cost of printing and carrying paper surveys for each interview, as well as the cost of training and hiring data entry personnel.

Creating local capacity

The need for capacity development is the most cited constraint to the adoption of electronic surveys among participants . Survey managers need highly specialized skills, and although the capacity exists in many low-income countries, it tends to be concentrated in research institutions and NGOs. 

The World Bank and other institutions that already have extensive experience using electronic data collection methods can help relieve this constraint with relative ease by sharing knowledge and resources.  Event participants highlighted their interest in further training for themselves as well as their recommendation that their colleagues also be trained. 

Improving accessibility

Using open-source software like ODK that allows for offline data collection makes computer-assisted data collection accessible. However, language can be a constraint when it comes to widening its user base. Survey administration interfaces are only available in English, a big accessibility barrier during our training in a French-speaking context. Most ODK-based systems allow for the utilization of multiple languages during data collection. Still, this feature is not easily accessible in the programming and setup interface, making it hard for the survey managers to navigate the platform. Solving this issue is up to the platforms themselves, and it will become more viable as the demand for this service increases among non-English speakers.

Where do we go from here?

While we listed multiple challenges, our experience shows that the demand for improved data collection methods exists. Participants have reported interest in conducting more electronic surveys and have recommended that other colleagues do the same.

Understanding the constraints to adopting these methods helps us target them directly. Knowledge sharing addresses three of the four constraints for the adoption of electronic data collection methods we have identified. Strengthening such efforts can go a long way in improving data quality in developing country contexts with relatively low costs.