5 Things to Consider for Development Aid Organizations Doing Mobile Data Collection

5 Things to Consider When Doing Mobile Data Collection

Development organizations now have a plethora of mobile data collection tools at their disposal, and choosing between the various options available can often seem like a daunting task. But even after finding the right tool, it is imperative to think through and address the most common challenges that arise in the mobile data collection process. In the last five years of helping development actors integrate mobile phones into their data collection processes, we’ve identified five challenges they run into most often.

1. Sampling Technique

Development organizations must ensure their sampling techniques meet high quality standards. When we refer to sampling, we mean ensuring that the target group of people who are surveyed accurately represents the population as a whole. Market research teams often use these techniques, but those techniques also apply in the development aid world, especially in organizations conducting programming and research. Keep in mind there is no such thing as a perfect sample. Statistical sampling will always have a margin of error that can be minimized by increasing the sample size.

Although a number of sampling techniques are available, the most commonly used is random sampling. Other sampling techniques include convenience, cluster, quota, and systematic. You can find additional pros and cons of these methods here.

However, if you’re simply looking to reach specific people (for example, people who have received direct aid), then sampling methodologies do not need to be taken into account.

2. Wide Geographic Span

Many sampling techniques can often result in a wide geographic span of respondent locations. The advantage of using mobile data collection tools is that they can often reduce the cost of reaching geographically dispersed sample populations. However, it is important to ensure your approach remains technologically agnostic. For example, some people may be in regions of a country that have no internet but have mobile reception. Your data collection tool should then be able to use interactive voice response (IVR) (which is also highly effective for those who can’t read), text messages, or unstructured supplementary service data (USSD) to gather the appropriate data. In regions where no mobile coverage exists, people can only be reached by deploying mobile-enabled field workers.

3. Many Languages / Dialects

Language is always a concern when collecting data remotely, since many languages and dialects must be accounted for. Artificial intelligence is bringing a wave of affordable translation technology, such as Google Translate, to our fingertips. However, these tools have a long way to go before they can claim 100 percent accuracy; moreover, they don’t cover many dialects. It is important to select a data collection platform that can handle language translation. Mobile data companies often accomplish translations via a hybrid of automated translation software and manual translation.

4. Fraudulent Data

In our work with multiple development clients, the subject of data fraud often arises. Fraud, like margins of error, can only be minimized, and not entirely eliminated. It’s best to choose a mobile data technology that uses fraud detection techniques such as pattern recognition, speeding, and red herring questions. Another technique to alleviate fraud is to ask respondents a previously asked question a second time several days later, where the software knows the expected response. This and other techniques, including national ID checks (where government databases are available), are all ways of decreasing fraud.

5. Respondent Drop-off

A final challenge that development organizations face is when conducting tracking studies (i.e. when the same person is surveyed more than once), and that person is non-responsive the second time. This is often handled by incentivizing the respondent through a number of mechanisms such as raffles, instant airtime, and instant cash, among others. Various techniques from the market research world including gamification (using gaming techniques such as points and leaderboards) are being increasingly used to incentivize participants during data collection.

In Conclusion…

Ultimately, with strong data collection technology, effective sampling, and accounting for the above challenges, a development organization will satisfy the growing demand for data-based metrics while also keeping up with today’s technological revolution.

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