Reports tab
Sometimes all the filters which are available in Causal Map can be bewildering and perhaps you just want to get your report finished already. The main purpose of this new tab is to provide an overview of a mapfile which makes sense and is immediately useful without any complicated searching or setting of filters. It provides for example sample description tables and some of the most useful tables which you can just copy and paste as needed. It’s work in progress, so let us know what you want.
We do not plan to provide much customisation of these outputs. Possibly the different outputs here could also provide links to the existing, equivalent filters in the rest of the app, so the user can tweak them there instead.
Export to Word and PDF files might be possible in the future.
Here we describe the different sub-tabs. It’s work in progress…
Sample
List total numbers and %s for all key groups, i.e. columns in the sources table which begin with either # or x_.
Opposites
If the mapfile includes opposites coding, show a maps in which opposites are combined.
If the mapfile includes hierarchical coding, show a map in which are opposites are combined, then zoomed out to level 1.
Rankings
If the sources table (or statements table) includes any columns which end with the word “rank” or “rank#”, then a QuIP-like table of ranked organisations is displayed in one of the sub-tabs.
Differences - questions
Shows which links were preferentially mentioned in answer to different questions.
Differences - sample
Shows which links were preferentially mentioned according to different groups e.g. women more than men. We ask:
does the proportion of women vs men who mention this link differ from what you would expect (given the total number of mentions of links by both women vs men)?
| Women | Men | |
|---|---|---|
| … other links … | ||
| Number of mentions of the link from X to Y | 10 | 9 |
| … other links … | ||
| Total number of mentions of any link | 60 | 10 |
In this case we can see that although women mentioned the link slightly more often than men, women altogether mentioned links twice as often as men. So we can compare the number of mentions of the link with the number of “non-mentions” of the link. So we can work out this table (not shown).
| Women | Men | |
|---|---|---|
| Number of mentions of the link from X to Y | 10 | 9 |
| Number of mentions of any other link | 50 | 1 |
We can do a simple chi-squared test on this table to see if the ratio 10:9 is significantly different from 50:1 (which of course it is) – this is the same question as to whether 10:50 is significantly different from 9:1 (which of course it is). If this test is significant, the row “Number of mentions of the link from X to Y” is shown in the table, and the intensity of the colouring of each cell reflects its chi-squared residual, i.e. how different is the number it contains from the number you would expect, given the other numbers?
This comparison is agnostic as to whether there are, say, many men or a few men who talk a lot.
At the moment, the tests for this are chi-squared tests which would not give special treatment groups which are actually ordinal e.g. low income, medium income, high income: the chi-squared test is weaker than it should be.