SenseMaker® is an innovative mixed qualitative/quantitative research tool. Its fundamental principle, the collection of self-interpreted narratives, is based on the recognition that storytelling is a natural way to convey complex information and is used by individuals to make sense of their own and their community’s experiences.
Figure 2. Example of a triad question.
Using SenseMaker, participants audio-record a story in response to an open-ended prompting question, thus generating the qualitative data. After the recording, participants then interpret their own experiences by plotting their perspectives between three variables (triads) or using sliders (dyads) (see Appendix for examples). SenseMaker then quantifies each of the plotted points, providing statistical data backed up by the accompanying explanatory narratives. Multiple-choice questions at the end of the survey collect demographic information and help to contextualize the shared story (e.g., what is the emotional tone of the story, how often do the events in the story happen, who was the story about, etc.).
By collecting a large number of self-interpreted stories, SenseMaker leverages the "wisdom of the crowds," and collectively, the participants' interpretation responses create a nuanced picture in the same way pixels come together to produce a clear image. SenseMaker surveys can be readily scaled up for use across multiple sites since once implemented, data collection and analysis costs are similar regardless of the number of sites included or the quantity of narratives uploaded.
Figure 3. Example of a dyad or slider question.
SenseMaker offers several unique advantages. First, it tends to provide a more comprehensive understanding of complex issues by using indirect prompting questions to elicit more honest and more revealing responses. Because it avoids asking direct questions, SenseMaker allows stories to emerge from the broader landscape of experiences, thus situating them in the everyday lives of participants. Indirect questioning has proven particularly useful when studying sensitive topics such as gender-based violence (GBV). Second, in contrast to quantitative surveys, which ask participants to choose between several discrete options (e.g., a Likert scale), SenseMaker provides more nuanced data because it allows for a much more extensive range of possible responses. Third, SenseMaker reduces social desirability bias because, within a given question, the possible responses are either all positive, all negative, or all neutral, with no one response being more socially acceptable or more desirable.
Figure 1. Example of a story prompt.
Fourth, interpretation bias is also reduced because participants interpret their own stories rather than researchers interpreting the narratives with their own inherent biases. Finally, because SenseMaker data is collected digitally on hand-held tablets or smartphones and the narratives are audio recorded, it is a very efficient method of conducting mixed-methods research. A typical SenseMaker survey will usually take about 15 minutes to complete, and the collection of hundreds of self-interpreted narratives is feasible each week. Since the quantitative data are available almost immediately after upload to the secure server, the results can be reviewed on an almost real-time basis, which offers a distinct advantage in contexts where prompt data is required for timely and responsive decision-making.
Describe how living near a UN base has provided either a particular opportunity or a specific danger to a woman or girl in your community. What happened?