Understanding organizational context requires an approach that captures both the richness of stories and the structure of quantitative data. The self-signified narrative method — where storytellers code their own stories through closed questions — offers a unique concurrent mixed method design that honors both depth and analytical rigor. Stories as Self-Coded Data Unlike traditional mixed methods where quantitative and qualitative data are collected through separate instruments, this approach integrates both within a single collection moment. After sharing their narrative, storytellers themselves answer closed questions about their own story. As Cynthia Kurtz explains in Working with Stories (2014), this self-signification process respects the storyteller’s interpretation while creating structured, analyzable data. The Method in Practice The concurrent collection works as follows: 1. Narrative elicitation — The storyteller shares their experience in their own words 2. Self-signification — The storyteller then answers closed questions about their story, which may include: ∙ Scale questions (e.g., “How much control did you feel in this situation?” rated 1-10) ∙ List selections (e.g., “Which factors influenced this outcome?” with predefined options) ∙ Triangle or matrix questions (e.g., positioning the story along multiple dimensions simultaneously) This approach ensures that the context of both the story and the storyteller is systematically explored. The storyteller — not the researcher — codes the narrative, preserving authenticity while generating quantitative data. Why Self-Signification Matters Kurtz emphasizes that “people are experts on their own experience” (Kurtz, 2014, p. 89). When storytellers code their own stories, several advantages emerge: ∙ Authenticity preserved — The meaning-making remains with the person who lived the experience ∙ Context captured — Closed questions can explore dimensions that might not emerge spontaneously in the narrative