This extension is about using factor labels to unify many “different looking” factors into one . Collapse factor labels : define one or more search terms, and any factor label that matches is rewritte…

Comparing groups Do men mention X more than women? Do project A respondents talk about different consequences than project B? This page sketches the methods you'll usually use in the Causal Map app, t…

Summary This extension is about filtering links using metadata (information attached to links and/or their sources), not just factor labels. Typical examples: include only links from a particular dist…

Summary This extension is about using factor labels to carve out a useful subgraph of your causal map. Like most extensions, it is best thought of as: 1) A filter (a rule that takes one links table an…

Simplifying large causal maps with hierarchical labels (zooming) !image.png Summary When you code lots of sources, you quickly end up with too many near duplicate causes and effects (and therefore too…

SOURCE NOTES (consolidation): The hierarchical coding / zooming material is now integrated into 005 Minimalist coding for causal mapping ((minimalist)) (section “Hierarchical coding and ‘zooming’ (FIL…

Summary Opposites coding is for when your data naturally contains paired factors like: and and and If you treat those as unrelated labels, you can miss half the evidence when you search/filter/analyse…

Summary Path tracing is for answering questions like: “ How does A lead to B (through what intermediate steps)?” “What are the main routes from an intervention to an outcome?” “If we start from this d…

Summary This extension is about simplifying a causal map by keeping only the most frequently mentioned : links (really: bundles of co terminal links, i.e. repeated claims with the same cause and effec…