During my bachelor’s studies, I developed a strong interest in user interactions and always took the lead in those parts of projects. As a people-oriented person, I particularly enjoyed direct interactions and primarily collected qualitative data through interviews. During my master’s program, I not only refined my interviewing and qualitative data analysis skills but also expanded my expertise by incorporating quantitative data collection methods in my work.
My experience with managing large sets of qualitative data began in my first master project. I gathered data from over 70 participants through an online questionnaire, which we then thematically analysed. I worked with even more data during the course Community Experience Design, where I used the crowdsourcing platform MTurk to collect over 250 responses. This experience was particularly valuable, as I had to do serious preprocessing on the raw data before conducting the analysis, which was very different from my experiences with mostly interview data.
In my final master project (FMP), I deepened my understanding of quantitative data analysis further, working with data from both sensors and user evaluations. I utilized three different breathing sensors, developing Python programs to filter and clean their data, as well visualizing and analysing it in Processing. This involved implementing techniques such as running averages to reduce noise and creating peak detection programs to enable sensor comparisons.
Additionally, during my final user test, I employed three established questionnaires to gather insights into various aspects of Slumber. For example, I used the User Experience Questionnaire (UEQ) to compare results from a previous user test of Slumber and against a benchmark. This process helped me develop a deeper understanding of quantitative data analysis, including the steps necessary to make data interpretable.
Relevance to Identity and Vision
Being able to work efficiently with insights gained from user evaluations is important to streamline the design process. I got a lot of experience in doing this throughout my master, by collecting different types of data and making sense of them through various methods. These experiences allow me to better understand the insights I will gain from user interactions in the future.
Peak detection on mmWave Radar data during FMP.
Sankey Diagram of MTurk responses during Community Experience Design.
Mapping of questionnaire responses during project 1.
Comparison of mmWave radar and Biosignalsplux data during FMP.
Thematic Analysis of interview transcription during FMP.