Research highlights academic, mentoring, and personal characteristics students associate with ideal supervision. The Graduate Student Experience Survey (GSES) invited graduate students from all disciplines to share their views on the qualities and characteristics of ideal supervision. The quantity and diversity responses posed a challenge: How can we systematically analyze textual data from diverse graduate students across campus? In this article, we describe the creation and application of a qualitative coding framework--a systematic method for categorizing and coding textual data--to synthesize 824 student responses to an open-ended survey question. We administered the GSES in 2022 and 2023 and conducted a quantitative content analysis and qualitative interpretation of 993 data extracts. Findings are organized into five categories: personal characteristics, teaching/mentoring, relational trust, professional support, and academic support. This deductive approach to qualitative analysis enabled us to identify trends and patterns in the traits graduate students most frequently associated with ideal supervision. These findings have practical applications: researchers can adapt the qualitative coding framework to analyze textual data, graduate students can use findings to identify suitable supervisors, and university leadership can leverage findings to improve supervisory development.