2017 ICT4D Conference

SenseMaker® is a research evaluation method that is specifically designed to monitor and explore complex processes from multiple human perspectives. SenseMaker(SM)® consists of several micro-narratives, questions, a pattern detection software and an underlying methodology that adheres to the basic principles of working in a complex space.

It consists of collecting micro-narrative which are interpreted by the respondent through a series of questions referred as signifiers. There are 3 important types of signifiers known as triads, stones and dyads. The data collection instrument is called the signification framework (SF). The SM® also includes a “pattern detection software” called Explorer that is used for analysis.

The USAID-funded Sustainable Mechanism for Improving Livelihood and Household Empowerment (SMILE) implemented by CRS utilized SM® approach to complement other methods of data collections in two out of the 5 implementation states during the projects mid-term review (MTR).

Micro-narratives in English and two local languages were obtained from 490 caregiver beneficiaries.

The SenseMaker® SF was uploaded in 9 mini iPads and each was assigned to an enumerator. It took approximately 45-90 minutes to interview a caregiver including listening to their stories, which are captured through the audio recording facility of the tool. The caregivers were able to tell their stories, which are captured verbatim by the device and stored, followed by questions relating to their stories or experience. The follow up questions are represented using triads, stones and dyads, which the caregivers make marks on the iPads to represent a measure of their comparative benefits or perception of SMILE intervention or other natural occurrences in their lives that have affected them positively or negatively in the past, among options depicted on the objects.

The use of SM® for the SMILE MTR is yet another example where technology provided an opportunity to analyze explanatory narratives in which cognitive bias is minimized thereby enhancing the reliance on the conclusions.