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…

!100 Task 3 Answering questions Individual questions/img/map 108 comparing groups what factors or links were mentioned more by some.png We can directly compare groups to find factors or links mentione…

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…

!100 Task 3 Answering questions Individual questions/img/map 113 focusing on specific factors what influences and outcomes are conn.png Focus on a particular element of your project to understand the…

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…

!100 Task 3 Answering questions Individual questions/img/screenshot 113 5 looking downstream what are the direct and indirect consequences.png Map out all direct and indirect paths flowing from select…

!100 Task 3 Answering questions Individual questions/img/screenshot 113 6 looking upstream what are the direct and indirect influences on.png Looking upstream is just the opposite of looking downstrea…

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…

What are the main causal pathways from an intervention to an outcome? We can trace chains of influence from a starting point like an intervention to a key outcome, revealing the step by step or branch…

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…

Analysing the sentiment expressed by your respondents and displaying it on your maps using positive (blue), negative (red) and ambivalent (grey) enables readers to easily identify positive and negativ…

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…

Source tracing is more conservative than path tracing and helps us avoid The transitivity trap ((transitivity trap)). !100 Task 3 Answering questions Individual questions/img/map 116 source tracing wh…

!100 Task 3 Answering questions Individual questions/img/screenshot 107 splitting by groups are different groups involved in different way.png The simplest way to compare groups is to make separate ca…

The Tribes filter answers a very specific analysis question: What are the most relevantly different subgroups in the data in terms of the causal stories they tell? In other words: if your sources cont…

!Pasted image 20251027172626.png The number of citations can be a useful measure of importance, but it can be distorted if one source mentions some factor a lot of times. A useful alternative is to co…

Similar to overall frequency counts in non causal coding ("which themes are mentioned most often?"), we can count how often particular causal factors are mentioned, and the number of sources mentionin…