Pages in this section:
Section 4: Analysis
Analysing a file
Screenshotting your maps
Filters: Tracing paths
Filters: Focus or exclude factors
Filters: Top factors and links
Filters: Combine opposites
Filters: Remove brackets
Filters: Collapse factors
Filters: Include or exclude hashtags
Formatters: Colour factor labels
The Links Table
The Sources Table
The Factors table
The Statements Table
The Mentions Table
The Questions Table
⚒️ The Closed Question Blocks Table
We code a causal map on the basis of text data. That text data can be usefully broken up into statements, usually of a length between a paragraph and a page. Each statement usually has “additional data” associated with it, for example the ID or gender of the respondent, the text of a question to which this statement is an answer, the page and name of the document from which this statement comes, etc. When we code a causal claim within a statement, we can associate the resulting link with the additional data. That means that for every link, we should know the additional data, e.g. the gender of the respondent, etc.
We call the set of statements corresponding to a particular value of a particular additional data field a “group”. This definition of “group” is quite broad and does not have to refer only to respondents, e.g. the group for “question 3” is the subset of all the data relevant to that question.
It is easy to filter a causal map by this additional data. This idea goes back at least to (Ford & Hegarty, 1984). For example, here is one map filtered to show all and only the links mentioned by with female respondents. We call these the per-value maps, e.g. the map consisting of all links mentioned by women. However, often the maps for different groups are quite similar as a large proportion of links are shared. When there are many links as in this example, the resulting filtered maps can be uninformative.
There may still be a bewildering hairball of links. We can apply techniques like hierarchical coding to “zoom out” of the map, or simply show only the most frequent factors. This map shows the top five factors for women:
And this map shows only the top five factors for men (there were far fewer men in this project).