The Open Sourcing Mental Health Survey was conducted in 2016. With over 1,400 responses, it aimed to examine the frequency of self-identified ‘mental health issues’ among tech workers by various responses (i.e. gender, occupation, location, company, etc). The data set can be found here. For our infographic, we focused specifically on work positions, locations (U.S. states), and types of self-identified mental health issues. The research questions we tried to solve were:
- How many survey respondents with a tech role currently claimed to have a mental health issue?
- Which US state has the most tech workers who identify a mental health issue?
- What types of tech roles had the most self-identified mental health issue?
- What type of mental health issues do tech employees self-identify?
We had a time constraint of two weeks to perform research, design, and write an analysis report. Our output was in a standard print size document (PDF) 11 x 17 "
To view the PDF version (for the optimal view): click here
We followed Few's taxonomy for plotting graphs. They're as follows:
- Classification of multiple distribution, parts to whole, and nominal comparison, respectively,
- Geospatial, and
- Classification of ranking and parts to whole.
We upheld Kosslyn's three goals for graph design as follows:
- Connecting with audience,
- Direct and hold attention, and
- Promote understanding and memory.
R graphics (R Studio), Adobe Illustrator, and Microsoft Excel.
This project was a two-person based team effort.
The team had following contributions:
- Carolyn Rojsutivat (Researcher & Writer): Carolyn performed all the research activities which included identifying (variables) & cleaning of the data set (using Microsoft Excel), Few's classification for plotting graphs, and writing analysis report.
- Abhinit Parelkar (Designer): I performed all the design activities which ranged from R-programming (plotting of the graphs), testing color palettes (using Sim Daltonism), designing 17 iterations of infographic (using Adobe Illustrator), and uphelding to Kosslyn's three goals for effective graph design.
The infographic result (with 17 iterations) was possible due to a strong collaboration between a researcher (Carolyn) and a designer (Abhinit).
The final iteration (17th) was possible due to the constructive feedback we received by the instructor and in-class critiques:
The feedbacks were based on the "Infographic output (8th iteration)" - See the image gallery. The compelling feedbacks are as follows:
- The unintentional use of offending/judgemental language "Mental Disorder/Mental Illness" under the graph titles - We learnt that representing the dataset in a right and acceptable way is the responsibility of a designer to deliver the values through design.
- The lack of story in the infographic - We learnt that adding a story gives user a context about what the infographic is trying to convey.
- We made the infographic more simple by removing the non-functional elements, for example, we removed the "red" colored texts which served no contextual meaning thus causing a visual clutter and imbalance.
We worked on the feedback and the final output could be observed in the image gallery as "Infographic output (17th iteration)". We'll be more than happy to recieve your feedback on it.