Data Collection:

• Qualitative Data: We collected qualitative data through the category names created by participants and their responses to the post-study questions, which provided feedback explaining their approach, reasoning, and reflections. This data provided valuable insights into how users mentally organize content, pain points, and their expectations for a user-friendly website. By analyzing the structure of their groupings alongside their explanation, we gained a deeper understanding of their decision-making processes, potential points of confusion, and opportunities to improve SocialOutfit.org navigation and organization.
• Quantitative Data: The quantitative data collected in this study focused on final groupings and frequencies of card placements made by participants. We analyzed this data in R Studio using a Hierarchical Cluster Analysis (HCA) and selected a dendrogram that resulted in an agglomerative coefficient (AC) close to a value of one that best visualized the data based on participant groupings. Additional analysis was conducted with a Multi-Dimensional Scaling (MDS) plot and a similarity matrix to visualize participant’s quantitative results to help identify patterns in how participants associated and grouped content. Overall, this provided us with a quantitative basis for understanding participants mental models and informed recommendations.

ANALYSIS

From a quantitative perspective, the analysis of card sorting data involved multiple steps to ensure a thorough understanding of how participants grouped the content and to derive actionable insights for the redesign of the SocialOutfit website. By utilizing Hierarchical Cluster Analysis (HCA), Multidimensional Scaling (MDS), and Similarity Matrix, we identified logical content groupings that align with user expectations and enhanced the website’s information architecture based on card sorting data from 23 participants.