📚 Glossary

ㅤ

Often there will be many links with the same influence and consequence factors. We call this set of links a bundle of links, which are usually mentioned in different statements by different sources. In maps, we display links in a bundle as one. A thicker line or a label can be used to show the number of links in the bundle.

Causal links are the links between the consequence and influence factors. Each link represents a casual claim.

A list of factor labels that can be used when coding causal links

When we combine any pairs of factors which are opposites. This means merging two factors which are identical apart from one starts with a ‘~’, to indicate a negative. On print view the link will show green and pink to indicate that both the positive and negative of the factor is true.

With a pair of opposite factors like Progress and ~Progress, we call Progress the ordinary factor and we call ~Progress the contrary factor. (We prefer this terminology to, say, “negative factor” as it has fewer unwanted connotations: contrary factors don’t have to be bad.)

A causal factor at the end of a link, that has been influenced by another factor.

a factor in Causal Map is a component of a causal link that represents something that can influence or be influenced by other factors. A factor is essentially its label.

Additional data associated with a statement such as respondent ID number, respondent age or question number.

A recurring subject found within the factor labels i.e. farming or health. Often we use symbols like # to make sure that the hashtags are uniquely searchable.

Nested factors (see below) put factor labels in a hierarchy from more general to specific. See more here.

(Mostly used in QuIP/BSDR coding.) When uploading data into Causal Map, a data table in hybrid format has some rows which are statements and some which are metadata.

A causal factor at the beginning of the link, that affects another factor.

a causal connection between two factors. Each link is associated with a specific quote from the text that provides evidence for that causal connection

feature in the app used for re-labeling and organizing factors. Magnetic labels attract existing labels of similar meaning, essentially relabeling these old labels with the new magnetic label. What we call soft-coding

When working in Causal Map, all your original data (the statements, source and question data) as well as the coding you have done (factors and links) are stored together in one file which we call a mapfile, or just file.

You can make short notes or memos on each statement.

Factor labels can be nested meaning there are higher level factors i.e New intervention; midwife training; hand washing instructions. 

A weblink (URL) to a causal map which has been shortened, usually to make sharing it easier. You can easily make shortlinks using the library.

A circular chain of causal connections where a factor influences itself

Refers to each individual piece of text to be coded in Causal Map, usually this is a sentence, a paragraph or a few paragraphs. Each statement has an ID like 1, 2, 3 etc.

Sources are where your statements come from, this could be respondents you have interviewed or documents you have collected.

Sigs = significance Significance level for showing a row is 0.1 and tables with less than 8 items are suppressed. Significance symbols: .=0.1, *=0.05, **=0.01, ***=0.001