Data Viz and Mixed Methods: How to Make Results Compelling

by |

By Maeve Conlin & Molly Ryan

Here at ICH, we often use a mixed methods design for our research and evaluation projects. A mixed method approach acknowledges the limitations of only using one type of data source. It involves collecting and analyzing qualitative data, typically from interviews or surveys, and quantitative data, like mortality rates.

Using multiple data sources helps to create a more complete and compelling picture of the data’s story. Mixed methods allow us to contextualize facts and figures, grounding them in the programs, projects, and communities they summarize. Perhaps most importantly, mixed methods facilitate a strength-based analysis, allowing for an exploration of opportunities as well as challenges.

Visualization Techniques to Connect Quantitative and Qualitative Data

Mixed methods are essential for much of our work at ICH, including all of our needs assessment projects. To conduct a needs assessment, we collect and analyze both quantitative data, like mortality causes, ED visits, and hospitalizations, along with community feedback on local health needs and solutions to health challenges. The result is A LOT of data! One strategy for helping your audience draw connections between your data is to include related quantitative and qualitative data side-by-side:

This same method of showing quantitative and qualitative data together can also be used for surveys, another tool we use frequently at ICH. For example, survey participants may be asked to rate their satisfaction with a variety of topics and explain their rating in a comment section. In this case, combining quantifiable participant satisfaction data with related quotes grounds the data and presents a fuller picture:

Making Qualitative Data Compelling

Within our qualitative data, we often look for ways to visually demonstrate similarities and differences across data points. As shown below, this can be done using a table format to display key themes. However, because this approach essentially quantifies qualitative data, we also include illustrative quotes so we do not to lose the interviewees’ voices or the richness of their comments.

Table 1: High-Risk Patient Definition by Site and Type of Respondent
Table 1: High-Risk Patient Definition by Site and Type of Respondent

Tailoring Data Visualization to Meet Unique Needs

Understanding data visualization processes and techniques helps us to present data that is not only eye-catching but easily understood. We can highlight important patterns and findings within a larger data set so that stakeholders can easily draw conclusions and make decisions.

However, having new and interesting ways to display data is not enough. Here at ICH, we work with a wide array of partners, including academia, hospitals, schools and community-based organizations. Exactly how and what data is presented, and to whom, are key considerations in the data visualization process. Most stakeholders or partners likely have different data needs, and it’s important to ensure you are presenting the information in a way that is comprehensible and useful for each unique project and audience!

The views expressed on the Institute for Community Health blog page are solely those of the blog post author(s), and do not necessarily reflect the views of ICH, the author’s employer or other organizations with which the author is associated.