Quant & qual linked analysis

Let open ends explain the numbers in the same workspace.

Connect coded open ends and structured survey data without creating parallel stories. Multi-language open ends, interview transcripts, and survey verbatims can sit alongside the study’s structured data as one evidence base.

A typical cut: respondents mentioning price concerns have a different NPS, over-index in a segment, and are significantly more likely to report onboarding difficulty — then you click through to see exactly what they said.

Ask questions across the whole evidence base.

Filter a quantitative cut by a qualitative theme—for example, show NPS among respondents who mentioned price concerns. Run related columns on a theme to see which structured variables associate with it, or include themes as drivers or clustering inputs alongside demographics and behaviours.

Give qualitative themes the same analytical treatment.

Pivot qual against quant with the same significance shading and chart types as standard survey variables. With every theme traceable to its verbatims and every quantitative cut testable against coded evidence, stakeholders can move from “what did people say?” to evidence they can inspect.

Keep one evidence base for the questions that follow.

The connection matters when the first answer creates a second question: whether a theme is concentrated in one segment, whether it relates to an outcome, or whether a reported score reflects a specific experience people described. AddMaple lets the analyst pursue those questions without exporting the coded data, manually joining respondent records, or losing the link to the original words.