Automated content analysis software tools have significantly aided in the study of design processes in the recent past. However, they suffer from the lack of domain knowledge and insight that a human expert can provide. In this paper, we adopt the use of text visualization techniques that help in gaining insights and identifying relevant patterns from the results obtained through a content analysis software. We motivate our approach with the observation that examining overall patterns in data aids us significantly in identifying interesting and relevant details concerning specific contexts in the data. We use the proposed approach to study the effect of adopting Laseauâ€™s â€œdesign funnelâ€ of alternating divergent and convergent design processes among student teams in a toy design course, and compare it to student teams that follow a free brainstorming process. We demonstrate the application of lexical dispersion plots and text concordances as a means to further examine the output of a conventional content analysis tool, and use these techniques to separate patterns from anomalies. We identify cases of concept consistency across teams using the dispersion plots, and identify cases of multiple word senses through text concordances. Finally, we present insights that were obtained through these visualizations and propose contexts for further studies of the data.