About Digital Data Collection

Digital Data Collection

Benefits of Digital Data Collection

Recording scientific observations predates KoBo by hundreds of years, so it is important to understand that the move to digital data collection is an enhancement to traditional scientific methodology, not a replacement. Researchers should consider carefully the type of research being conducted to determine if they can benefit from adapting to a digital process.

Research which collects data through face to face interviews, particularly large scale social science population surveys, are excellent candidates. In cases where the sample size is large enough to justify using more than a few enumerators and where surveys are complex (having a large number of questions; having some questions that require or disallow other questions) then the benefits can be significant.

Faster

Traditional surveys which use Pen & Paper involve an enumerator with a clipboard and a printed survey moving down a list of questions and recording the subjects answers. Individual interviews can be conducted faster, but more importantly, skipping traditional post-survey data entry speeds research from data collection to reporting.  

Cheaper

Though there is added cost in equipment, the savings in data entry costs, man hours, and the costs of printing and shipping paper surveys can significantly reduce the costs of collecting data, particularly in large scale surveys. 

More Accurate

KoBo's built in skip logic, branching based on question answers, and data constraints mean that every question is answered in the correct order. Surveyors cannot move to the next question without giving a complete answer that fits within the required constraints, and they can't go off-book by providing illegible or meaningless answers. 

Timestamps and GPS-based location stamps mean that the principal investigator knows where and when every survey was collected, as well as how long the survey took. Because there is no data-entry step, the principal investigator can review daily data dumps, looking for errors or irregularities. Flaws in sampling or enumerator's methodology can be fixed in the field, before they become problematic.